test(python): cover concurrent S3 table opens (#3833 )

## Summary - add regression coverage for the reported synchronous Python workload with 32 simultaneous `open_table` calls - verify every independently opened S3-backed table handle can read through the connection's shared session and object-store client ## Root cause In Python v0.13.0, each synchronous table handle lazily constructed its own Lance dataset. Opening many handles in parallel therefore triggered independent S3 client construction and bucket-region resolution, which failed under thread pressure. The current Rust-backed connection path owns a shared Lance session and retains its object-store handle, so table opens reuse the existing S3 client; these tests lock in that behavior through the public Python API and a causal Session-registry invariant. ## Validation - `uvx --from 'ruff==0.15.20' ruff format --check python/tests/test_s3.py` - `uvx --from 'ruff==0.15.20' ruff check .` - `cargo fmt --all` - `cargo test --quiet --features remote -p lancedb test_concurrent_open_table_reuses_connection_object_store` - `cargo check --quiet --features remote --tests --examples` - equivalent 32-thread `open_table(...).count_rows()` workload against a local database - targeted S3 test collected successfully locally; execution requires the CI LocalStack service, which is unavailable in this runner Fixes #1786  --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>
fix(remote): centralize timeline consistency (#4053 )
2026-08-26 07:58:31 +00:00 · 2026-08-26 14:56:57 +08:00 · 2026-08-26 12:54:36 +08:00 · 2026-08-25 21:56:26 -05:00 · 2026-08-26 10:08:48 +08:00 · 2026-08-25 16:49:17 -07:00
101 changed files with 8360 additions and 1305 deletions
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.38.0-beta.6"
+current_version = "0.38.0-beta.10"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@@ -3455,8 +3455,8 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"

 [[package]]
 name = "fsst"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "rand 0.9.5",
@@ -4815,8 +4815,8 @@ checksum = "e037a2e1d8d5fdbd49b16a4ea09d5d6401c1f29eca5ff29d03d3824dba16256a"

 [[package]]
 name = "lance"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arc-swap",
 "arrow",
@@ -4888,8 +4888,8 @@ dependencies = [

 [[package]]
 name = "lance-arrow"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4911,7 +4911,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-scalar"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4925,7 +4925,7 @@ dependencies = [
 [[package]]
 name = "lance-arrow-stats"
 version = "58.0.0"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -4934,8 +4934,8 @@ dependencies = [

 [[package]]
 name = "lance-bitpacking"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrayref",
 "crunchy",
@@ -4945,8 +4945,8 @@ dependencies = [

 [[package]]
 name = "lance-core"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -4983,8 +4983,8 @@ dependencies = [

 [[package]]
 name = "lance-datafusion"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5013,8 +5013,8 @@ dependencies = [

 [[package]]
 name = "lance-datagen"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5031,8 +5031,8 @@ dependencies = [

 [[package]]
 name = "lance-derive"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "proc-macro2",
 "quote",
@@ -5041,8 +5041,8 @@ dependencies = [

 [[package]]
 name = "lance-encoding"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -5075,8 +5075,8 @@ dependencies = [

 [[package]]
 name = "lance-file"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -5107,8 +5107,8 @@ dependencies = [

 [[package]]
 name = "lance-index"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arc-swap",
 "arrow",
@@ -5172,8 +5172,8 @@ dependencies = [

 [[package]]
 name = "lance-index-core"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5195,8 +5195,8 @@ dependencies = [

 [[package]]
 name = "lance-io"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5236,8 +5236,8 @@ dependencies = [

 [[package]]
 name = "lance-linalg"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5251,8 +5251,8 @@ dependencies = [

 [[package]]
 name = "lance-namespace"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "async-trait",
@@ -5264,8 +5264,8 @@ dependencies = [

 [[package]]
 name = "lance-namespace-impls"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-ipc",
@@ -5318,8 +5318,8 @@ dependencies = [

 [[package]]
 name = "lance-select"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -5333,8 +5333,8 @@ dependencies = [

 [[package]]
 name = "lance-table"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -5374,8 +5374,8 @@ dependencies = [

 [[package]]
 name = "lance-testing"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -5388,8 +5388,8 @@ dependencies = [

 [[package]]
 name = "lance-tokenizer"
-version = "11.0.0-beta.22"
-source = "git+https://github.com/lance-format/lance.git?tag=v11.0.0-beta.22#ea3cb4d799c468232735e9bcb43959487aca5c20"
+version = "12.0.0-beta.2"
+source = "git+https://github.com/lance-format/lance.git?tag=v12.0.0-beta.2#dafa4642658d996b3e31dde91e02f72db7860d7e"
 dependencies = [
 "frostem",
 "icu_segmenter",
@@ -5402,7 +5402,7 @@ dependencies = [

 [[package]]
 name = "lancedb"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.10"
 dependencies = [
 "ahash",
 "anyhow",
@@ -5490,7 +5490,7 @@ dependencies = [

 [[package]]
 name = "lancedb-nodejs"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.10"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -5515,7 +5515,7 @@ dependencies = [

 [[package]]
 name = "lancedb-python"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.10"
 dependencies = [
 "arrow",
 "async-trait",
@@ -13,20 +13,20 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"

 [workspace.dependencies]
-lance = { "version" = "=11.0.0-beta.22", default-features = false, "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=11.0.0-beta.22", default-features = false, "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=11.0.0-beta.22", default-features = false, "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=11.0.0-beta.22", "tag" = "v11.0.0-beta.22", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=12.0.0-beta.2", default-features = false, "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=12.0.0-beta.2", "tag" = "v12.0.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
 lancedb = { path = "rust/lancedb", default-features = false }
 ahash = "0.8"
 # Note that this one does not include pyarrow
@@ -14,7 +14,7 @@ Add the following dependency to your `pom.xml`:
 <dependency>
    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-core</artifactId>
-    <version>0.38.0-beta.6</version>
+    <version>0.38.0-beta.10</version>
 </dependency>
 ```

@@ -0,0 +1,518 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / AutoQuery
+
+# Class: AutoQuery
+
+A builder for automatic string searches.
+
+Automatic search determines whether to use full-text or vector search from
+the table revision selected for each execution. This builder exposes the
+common operations supported by both query families.
+
+## Extends
+
+- `StandardQueryBase`&lt;`NativeQuery` \| `NativeVectorQuery`&gt;
+
+## Properties
+
+### inner
+
+```ts
+protected inner: Query | VectorQuery | Promise<Query | VectorQuery>;
+```
+
+#### Inherited from
+
+`StandardQueryBase.inner`
+
+## Methods
+
+### analyzePlan()
+
+```ts
+analyzePlan(distributedMetrics?): Promise<string>
+```
+
+Executes the query and returns the physical query plan annotated with runtime metrics.
+
+This is useful for debugging and performance analysis, as it shows how the query was executed
+and includes metrics such as elapsed time, rows processed, and I/O statistics.
+
+#### Parameters
+
+* **distributedMetrics?**: [`AnalyzePlanDistributedMetrics`](../type-aliases/AnalyzePlanDistributedMetrics.md)
+    How distributed worker metrics are displayed for remote query plans.
+    Defaults to `"aggregate"`.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A query execution plan with runtime metrics for each step.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+
+const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
+
+Example output (with runtime metrics inlined):
+AnalyzeExec verbose=true, metrics=[]
+ ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
+  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
+   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
+    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
+     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
+      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
+       KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
+        LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
+```
+
+#### Inherited from
+
+`StandardQueryBase.analyzePlan`
+
+***
+
+### execute()
+
+```ts
+protected execute(options?): AsyncGenerator<RecordBatch<any>, void, unknown>
+```
+
+Execute the query and return the results as an
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`AsyncGenerator`&lt;`RecordBatch`&lt;`any`&gt;, `void`, `unknown`&gt;
+
+#### See
+
+ - AsyncIterator
+of
+ - RecordBatch.
+
+By default, LanceDb will use many threads to calculate results and, when
+the result set is large, multiple batches will be processed at one time.
+This readahead is limited however and backpressure will be applied if this
+stream is consumed slowly (this constrains the maximum memory used by a
+single query)
+
+#### Inherited from
+
+`StandardQueryBase.execute`
+
+***
+
+### explainPlan()
+
+```ts
+explainPlan(verbose): Promise<string>
+```
+
+Generates an explanation of the query execution plan.
+
+#### Parameters
+
+* **verbose**: `boolean` = `false`
+    If true, provides a more detailed explanation. Defaults to false.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A Promise that resolves to a string containing the query execution plan explanation.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
+```
+
+#### Inherited from
+
+`StandardQueryBase.explainPlan`
+
+***
+
+### fastSearch()
+
+```ts
+fastSearch(): this
+```
+
+Skip searching un-indexed data. This can make search faster, but will miss
+any data that is not yet indexed.
+
+Use [Table#optimize](Table.md#optimize) to index all un-indexed data.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.fastSearch`
+
+***
+
+### ~~filter()~~
+
+```ts
+filter(predicate): this
+```
+
+A filter statement to be applied to this query.
+
+#### Parameters
+
+* **predicate**: `string`
+
+#### Returns
+
+`this`
+
+#### See
+
+where
+
+#### Deprecated
+
+Use `where` instead
+
+#### Inherited from
+
+`StandardQueryBase.filter`
+
+***
+
+### fullTextSearch()
+
+```ts
+fullTextSearch(query, options?): this
+```
+
+#### Parameters
+
+* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+* **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.fullTextSearch`
+
+***
+
+### limit()
+
+```ts
+limit(limit): this
+```
+
+Set the maximum number of results to return.
+
+By default, a plain search has no limit.  If this method is not
+called then every valid row from the table will be returned.
+
+#### Parameters
+
+* **limit**: `number`
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.limit`
+
+***
+
+### offset()
+
+```ts
+offset(offset): this
+```
+
+Set the number of rows to skip before returning results.
+
+This is useful for pagination.
+
+#### Parameters
+
+* **offset**: `number`
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.offset`
+
+***
+
+### orderBy()
+
+```ts
+orderBy(ordering): this
+```
+
+Sort the results by the specified column(s).
+
+#### Parameters
+
+* **ordering**: [`ColumnOrdering`](../interfaces/ColumnOrdering.md) \| [`ColumnOrdering`](../interfaces/ColumnOrdering.md)[]
+
+#### Returns
+
+`this`
+
+This query builder.
+
+#### Inherited from
+
+`StandardQueryBase.orderBy`
+
+***
+
+### outputSchema()
+
+```ts
+outputSchema(): Promise<Schema<any>>
+```
+
+Returns the schema of the output that will be returned by this query.
+
+This can be used to inspect the types and names of the columns that will be
+returned by the query before executing it.
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+An Arrow Schema describing the output columns.
+
+#### Inherited from
+
+`StandardQueryBase.outputSchema`
+
+***
+
+### select()
+
+```ts
+select(columns): this
+```
+
+Return only the specified columns.
+
+By default a query will return all columns from the table.  However, this can have
+a very significant impact on latency.  LanceDb stores data in a columnar fashion.  This
+means we can finely tune our I/O to select exactly the columns we need.
+
+As a best practice you should always limit queries to the columns that you need.  If you
+pass in an array of column names then only those columns will be returned.
+
+You can also use this method to create new "dynamic" columns based on your existing columns.
+For example, you may not care about "a" or "b" but instead simply want "a + b".  This is often
+seen in the SELECT clause of an SQL query (e.g. `SELECT a+b FROM my_table`).
+
+To create dynamic columns you can pass in a Map<string, string>.  A column will be returned
+for each entry in the map.  The key provides the name of the column.  The value is
+an SQL string used to specify how the column is calculated.
+
+For example, an SQL query might state `SELECT a + b AS combined, c`.  The equivalent
+input to this method would be:
+
+#### Parameters
+
+* **columns**: `string` \| `string`[] \| `Record`&lt;`string`, `string`&gt; \| `Map`&lt;`string`, `string`&gt;
+
+#### Returns
+
+`this`
+
+#### Example
+
+```ts
+new Map([["combined", "a + b"], ["c", "c"]])
+
+Columns will always be returned in the order given, even if that order is different than
+the order used when adding the data.
+
+Note that you can pass in a `Record<string, string>` (e.g. an object literal). This method
+uses `Object.entries` which should preserve the insertion order of the object.  However,
+object insertion order is easy to get wrong and `Map` is more foolproof.
+```
+
+#### Inherited from
+
+`StandardQueryBase.select`
+
+***
+
+### toArray()
+
+```ts
+toArray(options?): Promise<any[]>
+```
+
+Collect the results as an array of objects.
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`Promise`&lt;`any`[]&gt;
+
+#### Inherited from
+
+`StandardQueryBase.toArray`
+
+***
+
+### toArrow()
+
+```ts
+toArrow(options?): Promise<Table<any>>
+```
+
+Collect the results as an Arrow
+
+#### Parameters
+
+* **options?**: `Partial`&lt;[`QueryExecutionOptions`](../interfaces/QueryExecutionOptions.md)&gt;
+
+#### Returns
+
+`Promise`&lt;`Table`&lt;`any`&gt;&gt;
+
+#### See
+
+ArrowTable.
+
+#### Inherited from
+
+`StandardQueryBase.toArrow`
+
+***
+
+### useLsm()
+
+```ts
+useLsm(enable): this
+```
+
+Control MemWAL read routing for this query.
+
+By default (unset), when the table carries a MemWAL write spec (see
+[Table#setLsmWriteSpec](Table.md#setlsmwritespec)), reads are routed through the LSM scanner so
+they also return data written via the `mergeInsert` LSM path that has not yet
+been compacted into the base table (the active/frozen in-memory memtables and
+the flushed generations), deduplicated by primary key; a table without a spec
+reads the base table.
+
+#### Parameters
+
+* **enable**: `boolean`
+    `true` forces the LSM scanner and errors if the table has no
+    MemWAL write spec. `false` bypasses the MemWAL and reads the base table only,
+    even when a spec is present.
+    Note: the LSM scanner does not support every query shape (e.g. reranking,
+    hybrid search, `orderBy`). On a MemWAL table those shapes error unless
+    `useLsm(false)` is set, because a base-only read would silently exclude
+    un-compacted MemWAL data.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.useLsm`
+
+***
+
+### where()
+
+```ts
+where(predicate): this
+```
+
+A filter statement to be applied to this query.
+
+The filter should be supplied as an SQL query string.  For example:
+
+#### Parameters
+
+* **predicate**: `string`
+
+#### Returns
+
+`this`
+
+#### Example
+
+```ts
+x > 10
+y > 0 AND y < 100
+x > 5 OR y = 'test'
+
+Filtering performance can often be improved by creating a scalar index
+on the filter column(s).
+
+Calling this multiple times combines the filters with a logical AND rather
+than replacing the previous filter.
+```
+
+#### Inherited from
+
+`StandardQueryBase.where`
+
+***
+
+### withRowId()
+
+```ts
+withRowId(): this
+```
+
+Whether to return the row id in the results.
+
+This column can be used to match results between different queries. For
+example, to match results from a full text search and a vector search in
+order to perform hybrid search.
+
+#### Returns
+
+`this`
+
+#### Inherited from
+
+`StandardQueryBase.withRowId`
@@ -584,6 +584,70 @@ Child namespace names and

 ***

+### listTables()
+
+#### listTables(options)
+
+```ts
+abstract listTables(options?): Promise<ListTablesResponse>
+```
+
+List a page of the tables in this database.
+
+To retrieve the tables after the page, pass the `pageToken` the response
+carries back in. A page can be shorter than `limit` without being the last
+one, so walk until a response carries no page token:
+
+```ts
+const names = [];
+let pageToken = undefined;
+do {
+  const page = await conn.listTables({ pageToken, limit: 100 });
+  names.push(...page.tables);
+  pageToken = page.pageToken;
+} while (pageToken);
+```
+
+##### Parameters
+
+* **options?**: `Partial`&lt;[`ListTablesOptions`](../interfaces/ListTablesOptions.md)&gt;
+    Pagination options
+    (`pageToken`, `limit`).
+
+##### Returns
+
+`Promise`&lt;[`ListTablesResponse`](../interfaces/ListTablesResponse.md)&gt;
+
+A page of table names and an
+  optional token for the tables after it.
+
+#### listTables(namespacePath, options)
+
+```ts
+abstract listTables(namespacePath?, options?): Promise<ListTablesResponse>
+```
+
+List a page of the tables in this database.
+
+##### Parameters
+
+* **namespacePath?**: `string`[]
+    The namespace path to list tables from
+    (defaults to root namespace)
+
+* **options?**: `Partial`&lt;[`ListTablesOptions`](../interfaces/ListTablesOptions.md)&gt;
+    Pagination options
+    (`pageToken`, `limit`).
+
+##### Returns
+
+`Promise`&lt;[`ListTablesResponse`](../interfaces/ListTablesResponse.md)&gt;
+
+A page of table names and an
+  optional token for the tables after it.
+
+***
+
 ### openMaterializedView()

 ```ts
@@ -660,7 +724,7 @@ a "not supported" error.

 ***

-### tableNames()
+### ~~tableNames()~~

 #### tableNames(options)

@@ -682,6 +746,10 @@ Tables will be returned in lexicographical order.

 `Promise`&lt;`string`[]&gt;

+##### Deprecated
+
+Use [Connection.listTables](Connection.md#listtables) instead.
+
 #### tableNames(namespacePath, options)

 ```ts
@@ -704,3 +772,7 @@ Tables will be returned in lexicographical order.
 ##### Returns

 `Promise`&lt;`string`[]&gt;
+
+##### Deprecated
+
+Use [Connection.listTables](Connection.md#listtables) instead.
@@ -942,7 +942,7 @@ Get the schema of the table.
 abstract search(
   query,
   queryType?,
-   ftsColumns?): Query | VectorQuery
+   ftsColumns?): Query | VectorQuery | AutoQuery
 ```

 Create a search query to find the nearest neighbors
@@ -964,7 +964,7 @@ of the given query

 #### Returns

-[`Query`](Query.md) \| [`VectorQuery`](VectorQuery.md)
+[`Query`](Query.md) \| [`VectorQuery`](VectorQuery.md) \| [`AutoQuery`](AutoQuery.md)

 ***

@@ -18,6 +18,7 @@

 ## Classes

+- [AutoQuery](classes/AutoQuery.md)
 - [BooleanQuery](classes/BooleanQuery.md)
 - [BoostQuery](classes/BoostQuery.md)
 - [BranchContents](classes/BranchContents.md)
@@ -100,6 +101,8 @@
 - [JobInfo](interfaces/JobInfo.md)
 - [ListNamespacesOptions](interfaces/ListNamespacesOptions.md)
 - [ListNamespacesResponse](interfaces/ListNamespacesResponse.md)
+- [ListTablesOptions](interfaces/ListTablesOptions.md)
+- [ListTablesResponse](interfaces/ListTablesResponse.md)
 - [LsmStats](interfaces/LsmStats.md)
 - [LsmWriteSpec](interfaces/LsmWriteSpec.md)
 - [MaterializedViewDefinition](interfaces/MaterializedViewDefinition.md)
@@ -0,0 +1,34 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ListTablesOptions
+
+# Interface: ListTablesOptions
+
+## Properties
+
+### limit?
+
+```ts
+optional limit: number;
+```
+
+An upper bound on how many tables to return.
+
+A page may hold fewer than this and still not be the last one, so keep
+going while the response carries a page token rather than while pages are
+full.
+
+***
+
+### pageToken?
+
+```ts
+optional pageToken: string;
+```
+
+Token from a previous response, to resume listing where it left off.
+
+The token is opaque: it carries whatever the database needs to resume, and
+callers should not construct or interpret one.
@@ -0,0 +1,23 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ListTablesResponse
+
+# Interface: ListTablesResponse
+
+## Properties
+
+### pageToken?
+
+```ts
+optional pageToken: string;
+```
+
+***
+
+### tables
+
+```ts
+tables: string[];
+```
@@ -4,11 +4,16 @@

 [@lancedb/lancedb](../globals.md) / TableNamesOptions

-# Interface: TableNamesOptions
+# Interface: ~~TableNamesOptions~~
+
+## Deprecated
+
+Use [ListTablesOptions](ListTablesOptions.md) with [Connection.listTables](../classes/Connection.md#listtables)
+instead.

 ## Properties

-### limit?
+### ~~limit?~~

 ```ts
 optional limit: number;
@@ -18,7 +23,7 @@ An optional limit to the number of results to return.

 ***

-### startAfter?
+### ~~startAfter?~~

 ```ts
 optional startAfter: string;
@@ -10,16 +10,12 @@
 function getRegistry(): EmbeddingFunctionRegistry
 ```

-Utility function to get the global instance of the registry
+Get the global embedding function registry.
+
+LanceDB built-in providers are initialized when this public API is first
+used, so importing the root package does not change automatic search
+selection for tables without embedding metadata.

 ## Returns

 [`EmbeddingFunctionRegistry`](../classes/EmbeddingFunctionRegistry.md)
-
-`EmbeddingFunctionRegistry` The global instance of the registry
-
-## Example
-
-```ts
-const registry = getRegistry();
-const openai = registry.get("openai").create();
@@ -261,6 +261,8 @@ instead of being materialized with the rest of the row.

 ::: lancedb.streaming.StreamingDataset

+::: lancedb.streaming.StreamingDataLoader
+
 ::: lancedb.permutation.permutation_builder

 ::: lancedb.permutation.PermutationBuilder
@@ -8,7 +8,7 @@
    <parent>
      <groupId>com.lancedb</groupId>
      <artifactId>lancedb-parent</artifactId>
-      <version>0.38.0-beta.6</version>
+      <version>0.38.0-beta.10</version>
      <relativePath>../pom.xml</relativePath>
    </parent>

@@ -6,7 +6,7 @@

    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.38.0-beta.6</version>
+    <version>0.38.0-beta.10</version>
    <packaging>pom</packaging>
    <name>${project.artifactId}</name>
    <description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,7 @@
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>11.0.0-beta.22</lance-core.version>
+        <lance-core.version>12.0.0-beta.2</lance-core.version>
        <spotless.skip>false</spotless.skip>
        <spotless.version>2.30.0</spotless.version>
        <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.10"
 publish = false
 license.workspace = true
 description.workspace = true
@@ -515,6 +515,137 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
        );
      });

+      it("will allow matching inferred types across records", function () {
+        expect(() =>
+          makeArrowTable([{ value: 1 }, { value: 2 }]),
+        ).not.toThrow();
+      });
+
+      it("will reject mismatched inferred types across records", function () {
+        expect(() => makeArrowTable([{ value: 1 }, { value: "two" }])).toThrow(
+          "Failed to infer schema for data. Previously inferred type Float64 but found Utf8 for field value at row 1. Consider providing an explicit schema.",
+        );
+      });
+
+      it("will ignore generated dictionary IDs when comparing inferred types", function () {
+        const table = makeArrowTable([{ str: "a" }, { str: "b" }], {
+          dictionaryEncodeStrings: true,
+        });
+
+        expect(table.getChild("str")?.toJSON()).toEqual(["a", "b"]);
+      });
+
+      it("will preserve null values without treating them as type mismatches", function () {
+        for (const records of [
+          [{ vector: [1, 2, 3] }, { vector: null }],
+          [{ vector: null }, { vector: [1, 2, 3] }],
+        ]) {
+          const table = makeArrowTable(records);
+
+          expect(table.numRows).toBe(2);
+          expect(table.getChild("vector")?.nullCount).toBe(1);
+        }
+      });
+
+      it("will preserve empty variable-size lists", function () {
+        for (const records of [
+          [{ items: [1] }, { items: [] }],
+          [{ items: [] }, { items: [1] }],
+        ]) {
+          const table = makeArrowTable(records);
+          expect(
+            table
+              .getChild("items")
+              ?.toJSON()
+              .map((value) => value.toJSON()),
+          ).toEqual(records.map((record) => record.items));
+        }
+      });
+
+      it("will propagate deferred evidence through nested lists", function () {
+        for (const records of [
+          [{ items: [1] }, { items: [null] }],
+          [{ items: [null] }, { items: [1] }],
+          [{ items: [null, 1] }, { items: [2, null] }],
+        ]) {
+          const table = makeArrowTable(records);
+          expect(
+            table
+              .getChild("items")
+              ?.toJSON()
+              .map((value) => value.toJSON()),
+          ).toEqual(records.map((record) => record.items));
+        }
+
+        const nestedRecords = [{ items: [[1]] }, { items: [[null]] }];
+        const nestedTable = makeArrowTable(nestedRecords);
+        expect(
+          nestedTable
+            .getChild("items")
+            ?.toJSON()
+            .map((value) =>
+              value
+                .toJSON()
+                .map((nestedValue: { toJSON: () => unknown[] }) =>
+                  nestedValue.toJSON(),
+                ),
+            ),
+        ).toEqual(nestedRecords.map((record) => record.items));
+      });
+
+      it("will reject incompatible deferred evidence within a list", function () {
+        for (const items of [
+          [[], 1],
+          [1, []],
+          [[null], 1],
+          [1, [null]],
+        ]) {
+          expect(() => makeArrowTable([{ items }])).toThrow(
+            "Failed to infer data type for field items at row 0.",
+          );
+        }
+      });
+
+      it("will reject empty fixed-size lists", function () {
+        expect(() =>
+          makeArrowTable([{ vector: [1, 2, 3] }, { vector: [] }]),
+        ).toThrow(
+          "Failed to infer schema for data. Previously inferred type FixedSizeList[3]<Float32> but found List[0] for field vector at row 1.",
+        );
+      });
+
+      it("will reject inferred leaf and branch shape changes", function () {
+        expect(() =>
+          makeArrowTable([{ value: 1 }, { value: { nested: 2 } }]),
+        ).toThrow(
+          "Failed to infer schema for data. Previously inferred type Float64 but found Struct for field value at row 1.",
+        );
+        expect(() =>
+          makeArrowTable([{ value: { nested: 1 } }, { value: 2 }]),
+        ).toThrow(
+          "Failed to infer schema for data. Previously inferred type Struct but found Float64 for field value at row 1.",
+        );
+      });
+
+      it("will allow null values around inferred struct values", function () {
+        for (const { records, nullIndex } of [
+          {
+            records: [{ value: null }, { value: { nested: 2 } }],
+            nullIndex: 0,
+          },
+          {
+            records: [{ value: { nested: 1 } }, { value: null }],
+            nullIndex: 1,
+          },
+        ]) {
+          const table = makeArrowTable(records);
+          const values = table.getChild("value");
+
+          expect(values?.nullCount).toBe(1);
+          expect(values?.get(nullIndex)).toBeNull();
+        }
+      });
+
      it("will allow a schema to be provided", async function () {
        await checkTableCreation(
          async (records, _, schema) =>
@@ -4,7 +4,13 @@
 import { readdirSync } from "fs";
 import { Field, Float64, Schema } from "apache-arrow";
 import * as tmp from "tmp";
-import { Connection, Table, connect, connectNamespace } from "../lancedb";
+import {
+  Connection,
+  ListTablesResponse,
+  Table,
+  connect,
+  connectNamespace,
+} from "../lancedb";
 import { LocalTable } from "../lancedb/table";

 describe("when connecting", () => {
@@ -47,6 +53,7 @@ describe("given a connection", () => {
    await db.close();
    expect(db.isOpen()).toBe(false);
    await expect(db.tableNames()).rejects.toThrow("Connection is closed");
+    await expect(db.listTables()).rejects.toThrow("Connection is closed");
    await expect(db.renameTable("a", "b")).rejects.toThrow(
      "Connection is closed",
    );
@@ -129,6 +136,66 @@ describe("given a connection", () => {
    expect(tables).toEqual(["b", "c"]);
  });

+  it("should respect limit and page token when listing tables", async () => {
+    const db = await connect(tmpDir.name);
+
+    await db.createTable("b", [{ id: 1 }]);
+    await db.createTable("a", [{ id: 1 }]);
+    await db.createTable("c", [{ id: 1 }]);
+
+    const all = await db.listTables();
+    expect(all.tables).toEqual(["a", "b", "c"]);
+    expect(all.pageToken).toBeUndefined();
+
+    const first = await db.listTables({ limit: 1 });
+    expect(first.tables).toEqual(["a"]);
+    expect(first.pageToken).toBeDefined();
+
+    const second = await db.listTables({
+      limit: 1,
+      pageToken: first.pageToken,
+    });
+    expect(second.tables).toEqual(["b"]);
+  });
+
+  it("should visit every table exactly once when walking pages", async () => {
+    const db = await connect(tmpDir.name);
+
+    const created = ["a", "b", "c", "d", "e"];
+    for (const name of created) {
+      await db.createTable(name, [{ id: 1 }]);
+    }
+
+    const seen: string[] = [];
+    let pageToken: string | undefined = undefined;
+    do {
+      const page: ListTablesResponse = await db.listTables({
+        limit: 2,
+        pageToken,
+      });
+      seen.push(...page.tables);
+      pageToken = page.pageToken;
+    } while (pageToken);
+
+    expect(seen).toEqual(created);
+  });
+
+  it("should list tables in a namespace", async () => {
+    const db = await connect(tmpDir.name, {
+      // biome-ignore lint/style/useNamingConvention: opaque backend property key, must match Rust
+      namespaceClientProperties: { manifest_enabled: "true" },
+    });
+    await db.createNamespace(["child"]);
+    await db.createTable("nested", [{ id: 1 }], ["child"]);
+
+    await expect(db.listTables(["child"])).resolves.toEqual(
+      expect.objectContaining({ tables: ["nested"] }),
+    );
+    await expect(db.listTables()).resolves.toEqual(
+      expect.objectContaining({ tables: [] }),
+    );
+  });
+
  it("should create tables in v2 mode", async () => {
    const db = await connect(tmpDir.name);
    const data = [...Array(10000).keys()].map((i) => ({ id: i }));
@@ -0,0 +1,95 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { execFileSync } from "node:child_process";
+import { resolve } from "node:path";
+
+import type { OpenAIEmbeddingFunction } from "../lancedb/embedding/openai";
+import type { EmbeddingFunctionRegistry } from "../lancedb/embedding/registry";
+
+type EmbeddingModule = typeof import("../lancedb/embedding");
+type OpenAIModule = typeof import("../lancedb/embedding/openai");
+type RegistryModule = typeof import("../lancedb/embedding/registry");
+
+describe("embedding function registry", () => {
+  const registries: EmbeddingFunctionRegistry[] = [];
+
+  afterEach(() => {
+    for (const registry of registries) {
+      registry.reset();
+    }
+    registries.length = 0;
+  });
+
+  it("defers built-in providers until the public registry API is used", () => {
+    jest.isolateModules(() => {
+      const embedding = require("../lancedb/embedding") as EmbeddingModule;
+      const { getRegistry: getInternalRegistry } =
+        require("../lancedb/embedding/registry") as RegistryModule;
+      const registry = getInternalRegistry();
+      registries.push(registry);
+
+      expect(registry.length()).toBe(0);
+      expect(embedding.getRegistry()).toBe(registry);
+      expect(registry.get("openai")).toBeDefined();
+      expect(registry.get("huggingface")).toBeDefined();
+    });
+  });
+
+  it("preserves automatic FTS search in a fresh process", () => {
+    execFileSync(
+      process.execPath,
+      [resolve(__dirname, "fixtures", "auto_fts_search.cjs")],
+      { stdio: "pipe" },
+    );
+  });
+
+  it("shares registrations across duplicated provider module graphs", () => {
+    let registeringRegistry: EmbeddingFunctionRegistry | undefined;
+    let latestOpenAIConstructor: typeof OpenAIEmbeddingFunction | undefined;
+
+    jest.isolateModules(() => {
+      require("../lancedb/embedding/openai");
+      const { getRegistry } =
+        require("../lancedb/embedding/registry") as RegistryModule;
+      registeringRegistry = getRegistry();
+      registries.push(registeringRegistry);
+      expect(registeringRegistry.get("openai")).toBeDefined();
+    });
+
+    expect(() => {
+      jest.isolateModules(() => {
+        const { OpenAIEmbeddingFunction } =
+          require("../lancedb/embedding/openai") as OpenAIModule;
+        latestOpenAIConstructor = OpenAIEmbeddingFunction;
+        const { getRegistry } =
+          require("../lancedb/embedding/registry") as RegistryModule;
+        registries.push(getRegistry());
+      });
+    }).not.toThrow();
+
+    const previousApiKey = process.env.OPENAI_API_KEY;
+    process.env.OPENAI_API_KEY = "test";
+    try {
+      const latestOpenAI = registeringRegistry!
+        .get<OpenAIEmbeddingFunction>("openai")!
+        .create();
+      expect(latestOpenAI).toBeInstanceOf(latestOpenAIConstructor!);
+    } finally {
+      if (previousApiKey === undefined) {
+        delete process.env.OPENAI_API_KEY;
+      } else {
+        process.env.OPENAI_API_KEY = previousApiKey;
+      }
+    }
+
+    jest.isolateModules(() => {
+      const { getRegistry } =
+        require("../lancedb/embedding") as EmbeddingModule;
+      const publicRegistry = getRegistry();
+      registries.push(publicRegistry);
+      expect(publicRegistry).toBe(registeringRegistry);
+      expect(publicRegistry.get("openai")).toBeDefined();
+    });
+  });
+});
@@ -0,0 +1,33 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+const assert = require("node:assert/strict");
+const tmp = require("tmp");
+const { connect, embedding, Index } = require("../../dist");
+const { getRegistry } = require("../../dist/embedding/registry");
+
+async function main() {
+  assert.equal(typeof embedding.getRegistry, "function");
+  assert.equal(getRegistry().length(), 0);
+  assert.equal(embedding.getRegistry(), getRegistry());
+  assert.equal(getRegistry().length(), 2);
+
+  const dir = tmp.dirSync({ unsafeCleanup: true });
+  let db;
+  try {
+    db = await connect(dir.name);
+    const table = await db.createTable("docs", [{ text: "hello world" }]);
+    await table.createIndex("text", { config: Index.fts() });
+
+    const rows = await table.search("hello").toArray();
+    assert.equal(rows[0].text, "hello world");
+  } finally {
+    db?.close();
+    dir.removeCallback();
+  }
+}
+
+main().catch((error) => {
+  console.error(error);
+  process.exitCode = 1;
+});
@@ -11,10 +11,13 @@ import * as arrow17 from "apache-arrow-17";
 import * as arrow18 from "apache-arrow-18";

 import {
+  AutoQuery,
  Connection,
  MatchQuery,
  PhraseQuery,
+  Query,
  Table,
+  VectorQuery,
  connect,
  tokenize,
 } from "../lancedb";
@@ -682,6 +685,56 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
  },
 );

+// https://github.com/lancedb/lancedb/issues/1963
+it("should query documents with LangChain PDF metadata", async () => {
+  const tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  try {
+    const db = await connect(tmpDir.name);
+    const documents = [
+      {
+        text: "first page",
+        vector: [1, 0],
+        source: "first.pdf",
+        loc: { pageNumber: 1, lines: { from: 1, to: 12 } },
+        pdf: {
+          version: "1.10.100",
+          info: {
+            format: "PDF 1.7",
+            producer: "pdf.js",
+            creator: "Writer",
+          },
+          totalPages: 2,
+        },
+      },
+      {
+        text: "second page",
+        vector: [0, 1],
+        source: "second.pdf",
+        loc: { pageNumber: 2, lines: { from: 13, to: 24 } },
+        pdf: {
+          version: "1.10.100",
+          info: {
+            format: "PDF 1.7",
+            producer: "pdf.js",
+            creator: "Writer",
+          },
+          totalPages: 2,
+        },
+      },
+    ];
+    const documentsTable = await db.createTable("documents", documents);
+
+    const results = await documentsTable.query().toArray();
+
+    expect(results).toHaveLength(2);
+    expect(results[0].source).toBe("first.pdf");
+    expect(results[0].pdf.info.producer).toBe("pdf.js");
+    expect(results[1].loc.pageNumber).toBe(2);
+  } finally {
+    tmpDir.removeCallback();
+  }
+});
+
 describe("merge insert", () => {
  let tmpDir: tmp.DirResult;
  let table: Table;
@@ -1777,6 +1830,194 @@ describe("Read consistency interval", () => {
  });
 });

+describe("automatic search schema consistency", () => {
+  let tmpDir: tmp.DirResult;
+
+  class SchemaRefreshEmbedding extends EmbeddingFunction<string> {
+    ndims() {
+      return 2;
+    }
+
+    embeddingDataType() {
+      return new Float32();
+    }
+
+    async computeSourceEmbeddings(data: string[]) {
+      return data.map((value) => [value.length, 1]);
+    }
+
+    async computeQueryEmbeddings(value: string) {
+      return [value.length, 1];
+    }
+  }
+
+  function embeddingSchema() {
+    const func = new SchemaRefreshEmbedding();
+    return LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+  }
+
+  beforeEach(() => {
+    getRegistry().reset();
+    register("schema-refresh")(SchemaRefreshEmbedding);
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+
+  afterEach(() => {
+    getRegistry().reset();
+    tmpDir.removeCallback();
+  });
+
+  it("uses the schema refreshed from another connection", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const stale = await first.createTable("docs", [{ text: "before" }], {
+        schema: embeddingSchema(),
+      });
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "after hello" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+
+      const search = stale.search("hello");
+      expect(search).toBeInstanceOf(AutoQuery);
+      expect(search).not.toBeInstanceOf(Query);
+      expect(search).not.toBeInstanceOf(VectorQuery);
+      expect("nprobes" in search).toBe(false);
+
+      const rows = await search.toArray();
+      expect(rows[0].text).toBe("after hello");
+      expect((await stale.schema()).metadata.has("embedding_functions")).toBe(
+        false,
+      );
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+
+  it("tracks embedding metadata across checkout and restore", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      await first.createTable("docs", [{ text: "before" }], {
+        schema: embeddingSchema(),
+      });
+      const table = await second.createTable(
+        "docs",
+        [{ text: "after hello" }],
+        { mode: "overwrite" },
+      );
+      await table.createIndex("text", { config: Index.fts() });
+
+      await table.checkout(1);
+      expect((await table.search("before").toArray())[0].text).toBe("before");
+
+      await table.checkoutLatest();
+      expect((await table.search("hello").toArray())[0].text).toBe(
+        "after hello",
+      );
+
+      await table.checkout(1);
+      await table.restore();
+      expect((await table.search("before").toArray())[0].text).toBe("before");
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+
+  it("pins automatic search while computing an embedding", async () => {
+    let markStarted!: () => void;
+    let releaseEmbedding!: () => void;
+    const started = new Promise<void>((resolve) => {
+      markStarted = resolve;
+    });
+    const released = new Promise<void>((resolve) => {
+      releaseEmbedding = resolve;
+    });
+
+    class BlockingEmbedding extends SchemaRefreshEmbedding {
+      async computeQueryEmbeddings(value: string) {
+        markStarted();
+        await released;
+        return [value.length, 1];
+      }
+    }
+
+    register("schema-refresh-blocking")(BlockingEmbedding);
+    const func = new BlockingEmbedding();
+    const schema = LanceSchema({
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const table = await first.createTable(
+        "docs",
+        [{ text: "hello before" }],
+        { schema },
+      );
+      const pending = table.search("hello").toArray();
+      await started;
+
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "hello after" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+      releaseEmbedding();
+
+      expect((await pending)[0].text).toBe("hello before");
+    } finally {
+      releaseEmbedding();
+      first.close();
+      second.close();
+    }
+  });
+
+  it("refreshes a reused automatic search for every execution", async () => {
+    const first = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+    const second = await connect(tmpDir.name, { readConsistencyInterval: 0 });
+
+    try {
+      const table = await first.createTable("docs", [
+        { text: "hello before", marker: "before" },
+      ]);
+      await table.createIndex("text", { config: Index.fts() });
+      const search = table.search("hello").select(["text"]);
+
+      const before = (await search.toArray())[0];
+      expect(before.text).toBe("hello before");
+      expect(before.marker).toBeUndefined();
+
+      const replacement = await second.createTable(
+        "docs",
+        [{ text: "hello after", marker: "after" }],
+        { mode: "overwrite" },
+      );
+      await replacement.createIndex("text", { config: Index.fts() });
+
+      const after = (await search.toArray())[0];
+      expect(after.text).toBe("hello after");
+      expect(after.marker).toBeUndefined();
+    } finally {
+      first.close();
+      second.close();
+    }
+  });
+});
+
 describe("schema evolution", function () {
  let tmpDir: tmp.DirResult;
  beforeEach(() => {
@@ -2344,7 +2585,24 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      );
    });

-    test("full text search if no embedding function provided", async () => {
+    test("full text search if only an unrelated embedding function is registered", async () => {
+      register("unused")(
+        class extends EmbeddingFunction<string> {
+          ndims() {
+            return 3;
+          }
+          embeddingDataType() {
+            return new Float32();
+          }
+          async computeQueryEmbeddings(_data: string) {
+            return [1, 2, 3];
+          }
+          async computeSourceEmbeddings(data: string[]) {
+            return data.map(() => [1, 2, 3]);
+          }
+        },
+      );
+
      const db = await connect(tmpDir.name);
      const data = [
        { text: "hello world", vector: [0.1, 0.2, 0.3] },
@@ -2366,6 +2624,306 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      expect(results2[0].text).toBe(data[1].text);
    });

+    test("auto search stays consistent with the active revision", async () => {
+      let initCalls = 0;
+      let queryCalls = 0;
+      let markStarted!: () => void;
+      const started = new Promise<void>((resolve) => {
+        markStarted = resolve;
+      });
+      let releaseEmbedding!: () => void;
+      const embeddingReleased = new Promise<void>((resolve) => {
+        releaseEmbedding = resolve;
+      });
+
+      @register("refresh-test")
+      class TestEmbedding extends EmbeddingFunction<string> {
+        async init() {
+          initCalls += 1;
+        }
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings(value: string) {
+          queryCalls += 1;
+          if (value === "blocked") {
+            markStarted();
+            await embeddingReleased;
+          }
+          return value === "greetings" ? [0.1] : [0.2];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map((value) =>
+            value === "hello world" ? [0.1] : [0.2],
+          );
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      await writer.createTable("test", [{ text: "plain", vector: [0.0] }]);
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("test");
+      type SnapshotCountingNative = {
+        querySnapshot: () => Promise<unknown>;
+      };
+      const native = (tracked as unknown as { inner: SnapshotCountingNative })
+        .inner;
+      const querySnapshot = native.querySnapshot.bind(native);
+      let snapshotCalls = 0;
+      native.querySnapshot = async () => {
+        snapshotCalls += 1;
+        return await querySnapshot();
+      };
+      const autoQuery = tracked.search("greetings").select(["text"]).limit(1);
+
+      const func = new TestEmbedding();
+      const schema = LanceSchema({
+        text: func.sourceField(new arrow.Utf8()),
+        vector: func.vectorField(),
+      });
+      const data = [{ text: "hello world" }, { text: "goodbye world" }];
+      await writer.createTable("test", data, { mode: "overwrite", schema });
+      const baselineInitCalls = initCalls;
+
+      expect(
+        (await tracked.schema()).metadata.get("embedding_functions"),
+      ).toBeDefined();
+      const results = await autoQuery.toArray();
+      expect(results[0].text).toBe(data[0].text);
+      expect(initCalls).toBe(baselineInitCalls + 1);
+      expect(queryCalls).toBe(1);
+      expect(snapshotCalls).toBe(1);
+
+      const repeatedResults = await autoQuery.toArray();
+      expect(repeatedResults[0].text).toBe(data[0].text);
+      expect(initCalls).toBe(baselineInitCalls + 1);
+      expect(queryCalls).toBe(1);
+      expect(snapshotCalls).toBe(2);
+
+      const pending = tracked
+        .search("blocked")
+        .select(["text"])
+        .limit(1)
+        .toArray();
+      await started;
+
+      const ftsData = [
+        { text: "greetings from full text", vector: [0.0] },
+        { text: "blocked from full text", vector: [0.0] },
+      ];
+      const ftsTable = await writer.createTable("test", ftsData, {
+        mode: "overwrite",
+      });
+      await ftsTable.createIndex("text", { config: Index.fts() });
+      releaseEmbedding();
+
+      const pendingResults = await pending;
+      expect(pendingResults[0].text).toBe(data[1].text);
+
+      expect(
+        (await tracked.schema()).metadata.get("embedding_functions"),
+      ).toBeUndefined();
+      const ftsResults = await autoQuery.toArray();
+      expect(ftsResults[0].text).toBe(ftsData[0].text);
+    });
+
+    test("auto search keeps newer preparation during a revision race", async () => {
+      let aCalls = 0;
+      let bCalls = 0;
+      let markAStarted!: () => void;
+      const aStarted = new Promise<void>((resolve) => {
+        markAStarted = resolve;
+      });
+      let releaseA!: () => void;
+      const aReleased = new Promise<void>((resolve) => {
+        releaseA = resolve;
+      });
+      let markBStarted!: () => void;
+      const bStarted = new Promise<void>((resolve) => {
+        markBStarted = resolve;
+      });
+      let releaseB!: () => void;
+      const bReleased = new Promise<void>((resolve) => {
+        releaseB = resolve;
+      });
+
+      @register("race-a")
+      class EmbeddingA extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          aCalls += 1;
+          markAStarted();
+          await aReleased;
+          return [0.1];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.1]);
+        }
+      }
+
+      @register("race-b")
+      class EmbeddingB extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          bCalls += 1;
+          markBStarted();
+          await bReleased;
+          return [0.2];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.2]);
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      const embeddingA = new EmbeddingA();
+      const schemaA = LanceSchema({
+        text: embeddingA.sourceField(new arrow.Utf8()),
+        vector: embeddingA.vectorField(),
+      });
+      await writer.createTable("race", [{ text: "revision a" }], {
+        schema: schemaA,
+      });
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("race");
+      const query = tracked.search("query");
+
+      const first = query.toArray();
+      await aStarted;
+
+      const embeddingB = new EmbeddingB();
+      const schemaB = LanceSchema({
+        text: embeddingB.sourceField(new arrow.Utf8()),
+        vector: embeddingB.vectorField(),
+      });
+      await writer.createTable("race", [{ text: "revision b" }], {
+        mode: "overwrite",
+        schema: schemaB,
+      });
+      const second = query.toArray();
+      await bStarted;
+
+      releaseA();
+      releaseB();
+      await Promise.all([first, second]);
+      expect(aCalls).toBe(1);
+      expect(bCalls).toBe(1);
+    });
+
+    test("stale FTS routing keeps newer vector preparation", async () => {
+      let vectorCalls = 0;
+      let markVectorStarted!: () => void;
+      const vectorStarted = new Promise<void>((resolve) => {
+        markVectorStarted = resolve;
+      });
+      let releaseVector!: () => void;
+      const vectorReleased = new Promise<void>((resolve) => {
+        releaseVector = resolve;
+      });
+
+      @register("stale-fts-race")
+      class RaceEmbedding extends EmbeddingFunction<string> {
+        ndims() {
+          return 1;
+        }
+        embeddingDataType() {
+          return new arrow.Float32();
+        }
+        async computeQueryEmbeddings() {
+          vectorCalls += 1;
+          markVectorStarted();
+          await vectorReleased;
+          return [0.1];
+        }
+        async computeSourceEmbeddings(values: string[]) {
+          return values.map(() => [0.1]);
+        }
+      }
+
+      const writer = await connect(tmpDir.name);
+      const ftsTable = await writer.createTable("stale_fts", [
+        { text: "hello", vector: [0.0] },
+      ]);
+      await ftsTable.createIndex("text", { config: Index.fts() });
+
+      const reader = await connect(tmpDir.name, {
+        readConsistencyInterval: 0,
+      });
+      const tracked = await reader.openTable("stale_fts");
+      type Snapshot = {
+        schema: () => Promise<Buffer>;
+      };
+      type NativeWithSnapshot = {
+        querySnapshot: () => Promise<Snapshot>;
+      };
+      const native = (tracked as unknown as { inner: NativeWithSnapshot })
+        .inner;
+      const querySnapshot = native.querySnapshot.bind(native);
+      let snapshotCalls = 0;
+      let markStaleSchemaStarted!: () => void;
+      const staleSchemaStarted = new Promise<void>((resolve) => {
+        markStaleSchemaStarted = resolve;
+      });
+      let releaseStaleSchema!: () => void;
+      const staleSchemaReleased = new Promise<void>((resolve) => {
+        releaseStaleSchema = resolve;
+      });
+      native.querySnapshot = async () => {
+        const snapshot = await querySnapshot();
+        snapshotCalls += 1;
+        if (snapshotCalls === 1) {
+          const schema = snapshot.schema.bind(snapshot);
+          snapshot.schema = async () => {
+            markStaleSchemaStarted();
+            await staleSchemaReleased;
+            return await schema();
+          };
+        }
+        return snapshot;
+      };
+
+      const query = tracked.search("hello");
+      const staleFtsExecution = query.toArray();
+      await staleSchemaStarted;
+
+      const embedding = new RaceEmbedding();
+      const vectorSchema = LanceSchema({
+        text: embedding.sourceField(new arrow.Utf8()),
+        vector: embedding.vectorField(),
+      });
+      await writer.createTable("stale_fts", [{ text: "hello" }], {
+        mode: "overwrite",
+        schema: vectorSchema,
+      });
+
+      const vectorExecution = query.toArray();
+      await vectorStarted;
+      releaseStaleSchema();
+      await staleFtsExecution;
+      releaseVector();
+      await vectorExecution;
+
+      await query.toArray();
+      expect(vectorCalls).toBe(1);
+    });
+
    test("tokenizes FTS queries by column or index name", async () => {
      const db = await connect(tmpDir.name);
      const data = [
@@ -2916,6 +3474,30 @@ describe("column name options", () => {
    expect(results[1].query_index).toBe(1);
  });

+  test("observes promised additional vectors while the query is pending", async () => {
+    const initialVector = new Promise<number[]>(() => undefined);
+    const query = table.query().nearestTo(initialVector);
+    const unhandled: unknown[] = [];
+    const onUnhandled = (reason: unknown) => unhandled.push(reason);
+    process.on("unhandledRejection", onUnhandled);
+
+    try {
+      query.addQueryVector(Promise.reject(new Error("extra vector failed")));
+      await new Promise<void>((resolve) => setImmediate(resolve));
+      expect(unhandled).toEqual([]);
+
+      const rejectedQuery = table
+        .query()
+        .nearestTo([0.1, 0.2])
+        .addQueryVector(Promise.reject(new Error("consumed vector failed")));
+      await expect(rejectedQuery.toArray()).rejects.toThrow(
+        "consumed vector failed",
+      );
+    } finally {
+      process.off("unhandledRejection", onUnhandled);
+    }
+  });
+
  test("index and search multivectors", async () => {
    const db = await connect(tmpDir.name);
    const data = [];
@@ -5,7 +5,6 @@ import {
  Data as ArrowData,
  Table as ArrowTable,
  Binary,
-  Bool,
  BufferType,
  DataType,
  DateUnit,
@@ -18,12 +17,7 @@ import {
  FixedSizeList,
  Float,
  Float32,
-  Float64,
  Int,
-  Int8,
-  Int16,
-  Int32,
-  Int64,
  LargeBinary,
  List,
  Null,
@@ -36,17 +30,16 @@ import {
  Struct,
  Timestamp,
  Type,
-  Uint8,
-  Uint16,
-  Uint32,
  Utf8,
  Vector,
  makeVector as arrowMakeVector,
+  util as arrowUtil,
  vectorFromArray as badVectorFromArray,
  makeBuilder,
  makeData,
 } from "apache-arrow";
 import { Buffers } from "apache-arrow/data";
+import { typedArrayToArrowType } from "./arrow_type";
 import { type EmbeddingFunction } from "./embedding/embedding_function";
 import {
  EmbeddingFunctionConfig,
@@ -59,14 +52,7 @@ import {
  sanitizeTable,
  sanitizeType,
 } from "./sanitize";
-
-/**
- * Check if a field name indicates a vector column.
- */
-function nameSuggestsVectorColumn(fieldName: string): boolean {
-  const nameLower = fieldName.toLowerCase();
-  return nameLower.includes("vector") || nameLower.includes("embedding");
-}
+import { inferSchema } from "./schema";

 export * from "apache-arrow";
 export type SchemaLike =
@@ -459,110 +445,6 @@ export function makeArrowTable(
  return new ArrowTable(inferredSchema, finalColumns);
 }

-function inferSchema(
-  data: Array<Record<string, unknown>>,
-  schema: Schema | undefined,
-  opts: MakeArrowTableOptions,
-): Schema {
-  // We will collect all fields we see in the data.
-  const pathTree = new PathTree<DataType>();
-
-  for (const [rowI, row] of data.entries()) {
-    for (const [path, value] of rowPathsAndValues(row)) {
-      if (!pathTree.has(path)) {
-        // First time seeing this field.
-        if (schema !== undefined) {
-          const field = getFieldForPath(schema, path);
-          if (field === undefined) {
-            throw new Error(
-              `Found field not in schema: ${path.join(".")} at row ${rowI}`,
-            );
-          } else {
-            pathTree.set(path, field.type);
-          }
-        } else {
-          const inferredType = inferType(value, path, opts);
-          if (inferredType === undefined) {
-            throw new Error(`Failed to infer data type for field ${path.join(
-              ".",
-            )} at row ${rowI}. \
-                             Consider providing an explicit schema.`);
-          }
-          pathTree.set(path, inferredType);
-        }
-      } else if (schema === undefined) {
-        const currentType = pathTree.get(path);
-        const newType = inferType(value, path, opts);
-        if (currentType !== newType) {
-          new Error(`Failed to infer schema for data. Previously inferred type \
-                     ${currentType} but found ${newType} at row ${rowI}. Consider \
-                     providing an explicit schema.`);
-        }
-      }
-    }
-  }
-
-  if (schema === undefined) {
-    function fieldsFromPathTree(pathTree: PathTree<DataType>): Field[] {
-      const fields = [];
-      for (const [name, value] of pathTree.map.entries()) {
-        if (value instanceof PathTree) {
-          const children = fieldsFromPathTree(value);
-          fields.push(new Field(name, new Struct(children), true));
-        } else {
-          fields.push(new Field(name, value, true));
-        }
-      }
-      return fields;
-    }
-    const fields = fieldsFromPathTree(pathTree);
-    return new Schema(fields);
-  } else {
-    function takeMatchingFields(
-      fields: Field[],
-      pathTree: PathTree<DataType>,
-    ): Field[] {
-      const outFields = [];
-      for (const field of fields) {
-        if (pathTree.map.has(field.name)) {
-          const value = pathTree.get([field.name]);
-          if (value instanceof PathTree) {
-            const struct = field.type as Struct;
-            const children = takeMatchingFields(struct.children, value);
-            outFields.push(
-              new Field(field.name, new Struct(children), field.nullable),
-            );
-          } else {
-            outFields.push(
-              new Field(field.name, value as DataType, field.nullable),
-            );
-          }
-        }
-      }
-      return outFields;
-    }
-    const fields = takeMatchingFields(schema.fields, pathTree);
-    return new Schema(fields);
-  }
-}
-
-function* rowPathsAndValues(
-  row: Record<string, unknown>,
-  basePath: string[] = [],
-): Generator<[string[], unknown]> {
-  for (const [key, value] of Object.entries(row)) {
-    if (isObject(value)) {
-      yield* rowPathsAndValues(value, [...basePath, key]);
-    } else {
-      // Skip undefined values - they should be treated the same as missing fields
-      // for embedding function purposes
-      if (value !== undefined) {
-        yield [[...basePath, key], value];
-      }
-    }
-  }
-}
-
 function isObject(value: unknown): value is Record<string, unknown> {
  return (
    typeof value === "object" &&
@@ -577,146 +459,19 @@ function isObject(value: unknown): value is Record<string, unknown> {
  );
 }

-function getFieldForPath(schema: Schema, path: string[]): Field | undefined {
-  let current: Field | Schema = schema;
+function valueAtPath(datum: Record<string, unknown>, path: string[]): unknown {
+  let current: unknown = datum;
  for (const key of path) {
-    if (current instanceof Schema) {
-      const field: Field | undefined = current.fields.find(
-        (f) => f.name === key,
-      );
-      if (field === undefined) {
-        return undefined;
-      }
-      current = field;
-    } else if (current instanceof Field && DataType.isStruct(current.type)) {
-      const struct: Struct = current.type;
-      const field = struct.children.find((f) => f.name === key);
-      if (field === undefined) {
-        return undefined;
-      }
-      current = field;
+    if (current == null) {
+      return null;
+    }
+    if (isObject(current) && (Object.hasOwn(current, key) || key in current)) {
+      current = current[key];
    } else {
      return undefined;
    }
  }
-  if (current instanceof Field) {
-    return current;
-  } else {
-    return undefined;
-  }
-}
-
-/**
- * Try to infer which Arrow type to use for a given value.
- *
- * May return undefined if the type cannot be inferred.
- */
-function inferType(
-  value: unknown,
-  path: string[],
-  opts: MakeArrowTableOptions,
-): DataType | undefined {
-  if (typeof value === "bigint") {
-    return new Int64();
-  } else if (typeof value === "number") {
-    // Even if it's an integer, it's safer to assume Float64. Users can
-    // always provide an explicit schema or use BigInt if they mean integer.
-    return new Float64();
-  } else if (typeof value === "string") {
-    if (opts.dictionaryEncodeStrings) {
-      return new Dictionary(new Utf8(), new Int32());
-    } else {
-      return new Utf8();
-    }
-  } else if (typeof value === "boolean") {
-    return new Bool();
-  } else if (value instanceof Buffer) {
-    return new Binary();
-  } else if (ArrayBuffer.isView(value) && !(value instanceof DataView)) {
-    const info = typedArrayToArrowType(value);
-    if (info !== undefined) {
-      const child = new Field("item", info.elementType, true);
-      return new FixedSizeList(info.length, child);
-    }
-    return undefined;
-  } else if (Array.isArray(value)) {
-    if (value.length === 0) {
-      return undefined; // Without any values we can't infer the type
-    }
-    if (path.length === 1 && Object.hasOwn(opts.vectorColumns, path[0])) {
-      const floatType = sanitizeType(opts.vectorColumns[path[0]].type);
-      return new FixedSizeList(
-        value.length,
-        new Field("item", floatType, true),
-      );
-    }
-    const valueType = inferType(value[0], path, opts);
-    if (valueType === undefined) {
-      return undefined;
-    }
-    // Try to automatically detect embedding columns.
-    if (nameSuggestsVectorColumn(path[path.length - 1])) {
-      // Check if value is a Uint8Array for integer vector type determination
-      if (value instanceof Uint8Array) {
-        // For integer vectors, we default to Uint8 (matching Python implementation)
-        const child = new Field("item", new Uint8(), true);
-        return new FixedSizeList(value.length, child);
-      } else {
-        // For float vectors, we default to Float32
-        const child = new Field("item", new Float32(), true);
-        return new FixedSizeList(value.length, child);
-      }
-    } else {
-      const child = new Field("item", valueType, true);
-      return new List(child);
-    }
-  } else {
-    // TODO: timestamp
-    return undefined;
-  }
-}
-
-class PathTree<V> {
-  map: Map<string, V | PathTree<V>>;
-
-  constructor(entries?: [string[], V][]) {
-    this.map = new Map();
-    if (entries !== undefined) {
-      for (const [path, value] of entries) {
-        this.set(path, value);
-      }
-    }
-  }
-  has(path: string[]): boolean {
-    let ref: PathTree<V> = this;
-    for (const part of path) {
-      if (!(ref instanceof PathTree) || !ref.map.has(part)) {
-        return false;
-      }
-      ref = ref.map.get(part) as PathTree<V>;
-    }
-    return true;
-  }
-  get(path: string[]): V | undefined {
-    let ref: PathTree<V> = this;
-    for (const part of path) {
-      if (!(ref instanceof PathTree) || !ref.map.has(part)) {
-        return undefined;
-      }
-      ref = ref.map.get(part) as PathTree<V>;
-    }
-    return ref as V;
-  }
-  set(path: string[], value: V): void {
-    let ref: PathTree<V> = this;
-    for (const part of path.slice(0, path.length - 1)) {
-      if (!ref.map.has(part)) {
-        ref.map.set(part, new PathTree<V>());
-      }
-      ref = ref.map.get(part) as PathTree<V>;
-    }
-    ref.map.set(path[path.length - 1], value);
-  }
+  return current;
 }

 function transposeData(
@@ -724,37 +479,26 @@ function transposeData(
  field: Field,
  path: string[] = [],
 ): Vector {
+  const valuesPath = [...path, field.name];
+  const values = data.map((datum) => valueAtPath(datum, valuesPath));
  if (field.type instanceof Struct) {
    const childFields = field.type.children;
-    const fullPath = [...path, field.name];
    const childVectors = childFields.map((child) => {
-      return transposeData(data, child, fullPath);
+      return transposeData(data, child, valuesPath);
    });
+    const nullCount = values.filter((value) => value === null).length;
    const structData = makeData({
      type: field.type,
+      length: values.length,
+      nullCount,
+      nullBitmap:
+        nullCount > 0
+          ? arrowUtil.packBools(values.map((value) => value !== null))
+          : undefined,
      children: childVectors as unknown as ArrowData<DataType>[],
    });
    return arrowMakeVector(structData);
  } else {
-    const valuesPath = [...path, field.name];
-    const values = data.map((datum) => {
-      let current: unknown = datum;
-      for (const key of valuesPath) {
-        if (current == null) {
-          return null;
-        }
-
-        if (
-          isObject(current) &&
-          (Object.hasOwn(current, key) || key in current)
-        ) {
-          current = current[key];
-        } else {
-          return null;
-        }
-      }
-      return current;
-    });
    return makeVector(values, field.type, undefined, field.nullable);
  }
 }
@@ -797,32 +541,6 @@ function makeListVector(lists: unknown[][]): Vector<unknown> {
  return listBuilder.finish().toVector();
 }

-/**
- * Map a JS TypedArray instance to the corresponding Arrow element DataType
- * and its length. Returns undefined if the value is not a recognized TypedArray.
- */
-function typedArrayToArrowType(
-  value: ArrayBufferView,
-): { elementType: DataType; length: number } | undefined {
-  if (value instanceof Float32Array)
-    return { elementType: new Float32(), length: value.length };
-  if (value instanceof Float64Array)
-    return { elementType: new Float64(), length: value.length };
-  if (value instanceof Uint8Array)
-    return { elementType: new Uint8(), length: value.length };
-  if (value instanceof Uint16Array)
-    return { elementType: new Uint16(), length: value.length };
-  if (value instanceof Uint32Array)
-    return { elementType: new Uint32(), length: value.length };
-  if (value instanceof Int8Array)
-    return { elementType: new Int8(), length: value.length };
-  if (value instanceof Int16Array)
-    return { elementType: new Int16(), length: value.length };
-  if (value instanceof Int32Array)
-    return { elementType: new Int32(), length: value.length };
-  return undefined;
-}
-
 /** Helper function to convert an Array of JS values to an Arrow Vector */
 function makeVector(
  values: unknown[],
@@ -1462,8 +1180,12 @@ export function ensureNestedFieldsExist(
          completeRow[field.name] = row[field.name];
        }
      } else {
-        // Field is missing from the data - set to null
-        completeRow[field.name] = null;
+        // Keep a missing struct valid while filling each of its children with
+        // null. This is distinct from an explicitly null struct value.
+        completeRow[field.name] =
+          field.type.constructor.name === "Struct"
+            ? ensureStructFieldsExist({}, field.type as Struct)
+            : null;
      }
    }

@@ -1498,8 +1220,12 @@ function ensureStructFieldsExist(
        completeStruct[childField.name] = data[childField.name];
      }
    } else {
-      // Field is missing - set to null
-      completeStruct[childField.name] = null;
+      // Keep a missing struct valid while filling each of its children with
+      // null. This is distinct from an explicitly null struct value.
+      completeStruct[childField.name] =
+        childField.type.constructor.name === "Struct"
+          ? ensureStructFieldsExist({}, childField.type as Struct)
+          : null;
    }
  }

@@ -0,0 +1,40 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import {
+  type DataType,
+  Float32,
+  Float64,
+  Int8,
+  Int16,
+  Int32,
+  Uint8,
+  Uint16,
+  Uint32,
+} from "apache-arrow";
+
+/**
+ * Map a JS TypedArray instance to the corresponding Arrow element type and
+ * length. Returns undefined when the view is not a supported TypedArray.
+ */
+export function typedArrayToArrowType(
+  value: ArrayBufferView,
+): { elementType: DataType; length: number } | undefined {
+  if (value instanceof Float32Array)
+    return { elementType: new Float32(), length: value.length };
+  if (value instanceof Float64Array)
+    return { elementType: new Float64(), length: value.length };
+  if (value instanceof Uint8Array)
+    return { elementType: new Uint8(), length: value.length };
+  if (value instanceof Uint16Array)
+    return { elementType: new Uint16(), length: value.length };
+  if (value instanceof Uint32Array)
+    return { elementType: new Uint32(), length: value.length };
+  if (value instanceof Int8Array)
+    return { elementType: new Int8(), length: value.length };
+  if (value instanceof Int16Array)
+    return { elementType: new Int16(), length: value.length };
+  if (value instanceof Int32Array)
+    return { elementType: new Int32(), length: value.length };
+  return undefined;
+}
@@ -31,12 +31,14 @@ import type {
  JobDescription,
  JobInfo,
  ListNamespacesResponse,
+  ListTablesResponse,
 } from "./native";
 export type {
  CreateNamespaceResponse,
  DescribeNamespaceResponse,
  DropNamespaceResponse,
  ListNamespacesResponse,
+  ListTablesResponse,
 };
 import { sanitizeTable } from "./sanitize";
 import { LocalTable, Table } from "./table";
@@ -134,6 +136,10 @@ export interface OpenTableOptions {
  indexCacheSize?: number;
 }

+/**
+ * @deprecated Use {@link ListTablesOptions} with {@link Connection.listTables}
+ * instead.
+ */
 export interface TableNamesOptions {
  /**
   * If present, only return names that come lexicographically after the
@@ -147,6 +153,24 @@ export interface TableNamesOptions {
  limit?: number;
 }

+export interface ListTablesOptions {
+  /**
+   * Token from a previous response, to resume listing where it left off.
+   *
+   * The token is opaque: it carries whatever the database needs to resume, and
+   * callers should not construct or interpret one.
+   */
+  pageToken?: string;
+  /**
+   * An upper bound on how many tables to return.
+   *
+   * A page may hold fewer than this and still not be the last one, so keep
+   * going while the response carries a page token rather than while pages are
+   * full.
+   */
+  limit?: number;
+}
+
 export interface ListNamespacesOptions {
  /** Token from a previous response for pagination. */
  pageToken?: string;
@@ -231,6 +255,7 @@ export abstract class Connection {
   * @param {Partial<TableNamesOptions>} options - options to control the
   * paging / start point (backwards compatibility)
   *
+   * @deprecated Use {@link Connection.listTables} instead.
   */
  abstract tableNames(options?: Partial<TableNamesOptions>): Promise<string[]>;
  /**
@@ -241,12 +266,53 @@ export abstract class Connection {
   * @param {Partial<TableNamesOptions>} options - options to control the
   * paging / start point
   *
+   * @deprecated Use {@link Connection.listTables} instead.
   */
  abstract tableNames(
    namespacePath?: string[],
    options?: Partial<TableNamesOptions>,
  ): Promise<string[]>;

+  /**
+   * List a page of the tables in this database.
+   *
+   * To retrieve the tables after the page, pass the `pageToken` the response
+   * carries back in. A page can be shorter than `limit` without being the last
+   * one, so walk until a response carries no page token:
+   *
+   * ```ts
+   * const names = [];
+   * let pageToken = undefined;
+   * do {
+   *   const page = await conn.listTables({ pageToken, limit: 100 });
+   *   names.push(...page.tables);
+   *   pageToken = page.pageToken;
+   * } while (pageToken);
+   * ```
+   *
+   * @param {Partial<ListTablesOptions>} options - Pagination options
+   *   (`pageToken`, `limit`).
+   * @returns {Promise<ListTablesResponse>} A page of table names and an
+   *   optional token for the tables after it.
+   */
+  abstract listTables(
+    options?: Partial<ListTablesOptions>,
+  ): Promise<ListTablesResponse>;
+  /**
+   * List a page of the tables in this database.
+   *
+   * @param {string[]} namespacePath - The namespace path to list tables from
+   *   (defaults to root namespace)
+   * @param {Partial<ListTablesOptions>} options - Pagination options
+   *   (`pageToken`, `limit`).
+   * @returns {Promise<ListTablesResponse>} A page of table names and an
+   *   optional token for the tables after it.
+   */
+  abstract listTables(
+    namespacePath?: string[],
+    options?: Partial<ListTablesOptions>,
+  ): Promise<ListTablesResponse>;
+
  /**
   * Open a table in the database.
   * @param {string} name - The name of the table
@@ -601,6 +667,25 @@ export class LocalConnection extends Connection {
    return await this.inner.listMaterializedViews();
  }

+  async listTables(
+    namespacePathOrOptions?: string[] | Partial<ListTablesOptions>,
+    options?: Partial<ListTablesOptions>,
+  ): Promise<ListTablesResponse> {
+    // Detect if first argument is namespacePath array or options object
+    const namespacePath = Array.isArray(namespacePathOrOptions)
+      ? namespacePathOrOptions
+      : undefined;
+    const listTablesOptions = Array.isArray(namespacePathOrOptions)
+      ? options
+      : namespacePathOrOptions;
+
+    return this.inner.listTables(
+      namespacePath ?? [],
+      listTablesOptions?.pageToken,
+      listTablesOptions?.limit,
+    );
+  }
+
  async openTable(
    name: string,
    namespacePath?: string[],
@@ -4,7 +4,15 @@
 import { Field, Schema } from "../arrow";
 import { sanitizeType } from "../sanitize";
 import { EmbeddingFunction } from "./embedding_function";
-import { EmbeddingFunctionConfig, getRegistry } from "./registry";
+import {
+  EmbeddingFunctionConfig,
+  EmbeddingFunctionRegistry,
+  getRegistry as getGlobalRegistry,
+  registerBuiltIn,
+} from "./registry";
+
+type OpenAIModule = typeof import("./openai");
+type TransformersModule = typeof import("./transformers");

 export {
  FieldOptions,
@@ -14,7 +22,39 @@ export {
  EmbeddingFunctionConstructor,
 } from "./embedding_function";

-export * from "./registry";
+export {
+  EmbeddingFunctionRegistry,
+  parseEmbeddingMetadata,
+  register,
+} from "./registry";
+export type {
+  CreateReturnType,
+  EmbeddingFunctionConfig,
+  EmbeddingFunctionCreate,
+  EmbeddingMetadataEntry,
+  ResolvedEmbeddingFunctionConfig,
+} from "./registry";
+
+function initializeBuiltInProviders() {
+  const { OpenAIEmbeddingFunction } = require("./openai") as OpenAIModule;
+  const { TransformersEmbeddingFunction } =
+    require("./transformers") as TransformersModule;
+
+  registerBuiltIn("openai", OpenAIEmbeddingFunction);
+  registerBuiltIn("huggingface", TransformersEmbeddingFunction);
+}
+
+/**
+ * Get the global embedding function registry.
+ *
+ * LanceDB built-in providers are initialized when this public API is first
+ * used, so importing the root package does not change automatic search
+ * selection for tables without embedding metadata.
+ */
+export function getRegistry(): EmbeddingFunctionRegistry {
+  initializeBuiltInProviders();
+  return getGlobalRegistry();
+}

 /**
 * Create a schema with embedding functions.
@@ -5,14 +5,13 @@ import type OpenAI from "openai";
 import type { EmbeddingCreateParams } from "openai/resources/index";
 import { Float, Float32 } from "../arrow";
 import { EmbeddingFunction } from "./embedding_function";
-import { register } from "./registry";
+import { registerBuiltIn } from "./registry";

 export type OpenAIOptions = {
  apiKey: string;
  model: EmbeddingCreateParams["model"];
 };

-@register("openai")
 export class OpenAIEmbeddingFunction extends EmbeddingFunction<
  string,
  Partial<OpenAIOptions>
@@ -100,3 +99,5 @@ export class OpenAIEmbeddingFunction extends EmbeddingFunction<
    return response.data[0].embedding;
  }
 }
+
+registerBuiltIn("openai", OpenAIEmbeddingFunction);
@@ -7,6 +7,10 @@ import {
 } from "./embedding_function";
 import "reflect-metadata";

+const builtInFunctionsKey = Symbol.for(
+  "@lancedb/lancedb::embedding-built-in-functions::v1",
+);
+
 export type CreateReturnType<T> = T extends { init: () => Promise<void> }
  ? Promise<T>
  : T;
@@ -59,6 +63,15 @@ export class EmbeddingFunctionRegistry {
    };
  }

+  /** @ignore */
+  setBuiltIn<
+    T extends EmbeddingFunctionConstructor = EmbeddingFunctionConstructor,
+  >(name: string, ctor: T): T {
+    this.#functions.set(name, ctor);
+    Reflect.defineMetadata("lancedb::embedding::name", name, ctor);
+    return ctor;
+  }
+
  get<T extends EmbeddingFunction<unknown>>(
    name: string,
  ): EmbeddingFunctionCreate<T> | undefined;
@@ -96,6 +109,7 @@ export class EmbeddingFunctionRegistry {
   */
  reset(this: EmbeddingFunctionRegistry) {
    this.#functions.clear();
+    getBuiltInFunctions(this).clear();
  }

  /**
@@ -183,12 +197,56 @@ export class EmbeddingFunctionRegistry {
  }
 }

-const _REGISTRY = new EmbeddingFunctionRegistry();
+function getBuiltInFunctions(registry: EmbeddingFunctionRegistry): Set<string> {
+  const registryWithBuiltIns = registry as EmbeddingFunctionRegistry & {
+    [key: symbol]: Set<string> | undefined;
+  };
+  let builtInFunctions = registryWithBuiltIns[builtInFunctionsKey];
+  if (builtInFunctions === undefined) {
+    builtInFunctions = new Set<string>();
+    registryWithBuiltIns[builtInFunctionsKey] = builtInFunctions;
+  }
+  return builtInFunctions;
+}
+
+// Server bundlers can load the side-effect embedding entry points and the public
+// embedding API from separate module graphs. Keep their registry shared.
+const registryKey = Symbol.for(
+  "@lancedb/lancedb::embedding-function-registry::v1",
+);
+const registryGlobal = globalThis as typeof globalThis & {
+  [key: symbol]: EmbeddingFunctionRegistry | undefined;
+};
+
+function getGlobalRegistry(): EmbeddingFunctionRegistry {
+  const existingRegistry = registryGlobal[registryKey];
+  if (existingRegistry !== undefined) {
+    return existingRegistry;
+  }
+  const registry = new EmbeddingFunctionRegistry();
+  registryGlobal[registryKey] = registry;
+  return registry;
+}
+
+const _REGISTRY = getGlobalRegistry();

 export function register(name?: string) {
  return _REGISTRY.register(name);
 }

+/** @ignore */
+export function registerBuiltIn<
+  T extends EmbeddingFunctionConstructor = EmbeddingFunctionConstructor,
+>(name: string, ctor: T): T {
+  const builtInFunctions = getBuiltInFunctions(_REGISTRY);
+  if (builtInFunctions.has(name)) {
+    return _REGISTRY.setBuiltIn(name, ctor);
+  }
+  _REGISTRY.register(name)(ctor);
+  builtInFunctions.add(name);
+  return ctor;
+}
+
 /**
 * Utility function to get the global instance of the registry
 * @returns `EmbeddingFunctionRegistry` The global instance of the registry
@@ -3,7 +3,7 @@

 import { Float, Float32 } from "../arrow";
 import { EmbeddingFunction } from "./embedding_function";
-import { register } from "./registry";
+import { registerBuiltIn } from "./registry";

 export type XenovaTransformerOptions = {
  /** The wasm compatible model to use */
@@ -31,7 +31,6 @@ export type XenovaTransformerOptions = {
  };
 };

-@register("huggingface")
 export class TransformersEmbeddingFunction extends EmbeddingFunction<
  string,
  Partial<XenovaTransformerOptions>
@@ -158,6 +157,8 @@ export class TransformersEmbeddingFunction extends EmbeddingFunction<
  }
 }

+registerBuiltIn("huggingface", TransformersEmbeddingFunction);
+
 const tensorDiv = (
  src: import("@huggingface/transformers").Tensor,
  divBy: number,
@@ -81,11 +81,13 @@ export {
  Connection,
  CreateTableOptions,
  TableNamesOptions,
+  ListTablesOptions,
  OpenTableOptions,
  ListNamespacesOptions,
  CreateNamespaceOptions,
  DropNamespaceOptions,
  ListNamespacesResponse,
+  ListTablesResponse,
  CreateNamespaceResponse,
  DropNamespaceResponse,
  DescribeNamespaceResponse,
@@ -101,6 +103,7 @@ export {
 } from "./native.js";

 export {
+  AutoQuery,
  ExecutableQuery,
  Query,
  QueryBase,
@@ -100,6 +100,29 @@ export interface FullTextSearchOptions {
  columns?: string | string[];
 }

+function nearestToNative(
+  inner: NativeQuery,
+  vector: Awaited<IntoVector>,
+): NativeVectorQuery {
+  const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
+  if (raw) {
+    return inner.nearestToRaw(raw.data, raw.dtype);
+  }
+  return inner.nearestTo(Float32Array.from(vector as number[]));
+}
+
+function addQueryVectorToNative(
+  inner: NativeVectorQuery,
+  vector: Awaited<IntoVector>,
+) {
+  const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
+  if (raw) {
+    inner.addQueryVectorRaw(raw.data, raw.dtype);
+  } else {
+    inner.addQueryVector(Float32Array.from(vector as number[]));
+  }
+}
+
 /** Common methods supported by all query types
 *
 * @see {@link Query}
@@ -111,13 +134,15 @@ export class QueryBase<
  NativeQueryType extends NativeQuery | NativeVectorQuery | NativeTakeQuery,
 > implements AsyncIterable<RecordBatch>
 {
+  protected inner!: NativeQueryType | Promise<NativeQueryType>;
+
  /**
   * @hidden
   */
-  protected constructor(
-    protected inner: NativeQueryType | Promise<NativeQueryType>,
-  ) {
-    // intentionally empty
+  protected constructor(inner?: NativeQueryType | Promise<NativeQueryType>) {
+    if (inner !== undefined) {
+      this.inner = inner;
+    }
  }

  // call a function on the inner (either a promise or the actual object)
@@ -135,6 +160,15 @@ export class QueryBase<
    }
  }

+  /**
+   * Return the native query used by the next terminal operation.
+   *
+   * @hidden
+   */
+  protected async getInner(): Promise<NativeQueryType> {
+    return this.inner;
+  }
+
  /**
   * Return only the specified columns.
   *
@@ -207,16 +241,11 @@ export class QueryBase<
  /**
   * @hidden
   */
-  protected nativeExecute(
+  protected async nativeExecute(
    options?: Partial<QueryExecutionOptions>,
  ): Promise<NativeBatchIterator> {
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) =>
-        inner.execute(options?.maxBatchLength, options?.timeoutMs),
-      );
-    } else {
-      return this.inner.execute(options?.maxBatchLength, options?.timeoutMs);
-    }
+    const inner = await this.getInner();
+    return inner.execute(options?.maxBatchLength, options?.timeoutMs);
  }

  /**
@@ -245,12 +274,7 @@ export class QueryBase<
  /** Collect the results as an Arrow @see {@link ArrowTable}. */
  async toArrow(options?: Partial<QueryExecutionOptions>): Promise<ArrowTable> {
    const batches = [];
-    let inner;
-    if (this.inner instanceof Promise) {
-      inner = await this.inner;
-    } else {
-      inner = this.inner;
-    }
+    const inner = await this.getInner();
    for await (const batch of new RecordBatchIterable(inner, options)) {
      batches.push(batch);
    }
@@ -279,11 +303,8 @@ export class QueryBase<
   * @returns A Promise that resolves to a string containing the query execution plan explanation.
   */
  async explainPlan(verbose = false): Promise<string> {
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) => inner.explainPlan(verbose));
-    } else {
-      return this.inner.explainPlan(verbose);
-    }
+    const inner = await this.getInner();
+    return inner.explainPlan(verbose);
  }

  /**
@@ -321,13 +342,8 @@ export class QueryBase<
    distributedMetrics?: AnalyzePlanDistributedMetrics,
  ): Promise<string> {
    const distributedMetricsMode = distributedMetrics ?? "aggregate";
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) =>
-        inner.analyzePlan(distributedMetricsMode),
-      );
-    } else {
-      return this.inner.analyzePlan(distributedMetricsMode);
-    }
+    const inner = await this.getInner();
+    return inner.analyzePlan(distributedMetricsMode);
  }

  /**
@@ -339,12 +355,8 @@ export class QueryBase<
   * @returns An Arrow Schema describing the output columns.
   */
  async outputSchema(): Promise<import("./arrow").Schema> {
-    let schemaBuffer: Buffer;
-    if (this.inner instanceof Promise) {
-      schemaBuffer = await this.inner.then((inner) => inner.outputSchema());
-    } else {
-      schemaBuffer = await this.inner.outputSchema();
-    }
+    const inner = await this.getInner();
+    const schemaBuffer = await inner.outputSchema();
    const schema = tableFromIPC(schemaBuffer).schema;
    return schema;
  }
@@ -356,7 +368,7 @@ export class StandardQueryBase<
  extends QueryBase<NativeQueryType>
  implements ExecutableQuery
 {
-  constructor(inner: NativeQueryType | Promise<NativeQueryType>) {
+  constructor(inner?: NativeQueryType | Promise<NativeQueryType>) {
    super(inner);
  }

@@ -510,6 +522,13 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    super(inner);
  }

+  /**
+   * @hidden
+   */
+  protected doVectorCall(fn: (inner: NativeVectorQuery) => void) {
+    super.doCall(fn);
+  }
+
  /**
   * Set the number of partitions to search (probe)
   *
@@ -537,7 +556,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * the minimum and maximum to the same value.
   */
  nprobes(nprobes: number): VectorQuery {
-    super.doCall((inner) => inner.nprobes(nprobes));
+    this.doVectorCall((inner) => inner.nprobes(nprobes));

    return this;
  }
@@ -551,7 +570,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * but will also increase latency.
   */
  minimumNprobes(minimumNprobes: number): VectorQuery {
-    super.doCall((inner) => inner.minimumNprobes(minimumNprobes));
+    this.doVectorCall((inner) => inner.minimumNprobes(minimumNprobes));
    return this;
  }

@@ -565,7 +584,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * potential false negatives.
   */
  maximumNprobes(maximumNprobes: number): VectorQuery {
-    super.doCall((inner) => inner.maximumNprobes(maximumNprobes));
+    this.doVectorCall((inner) => inner.maximumNprobes(maximumNprobes));
    return this;
  }

@@ -578,7 +597,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * `undefined` means no lower or upper bound.
   */
  distanceRange(lowerBound?: number, upperBound?: number): VectorQuery {
-    super.doCall((inner) => inner.distanceRange(lowerBound, upperBound));
+    this.doVectorCall((inner) => inner.distanceRange(lowerBound, upperBound));
    return this;
  }

@@ -592,7 +611,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * also increase the latency of your query. The default value is 1.5*limit.
   */
  ef(ef: number): VectorQuery {
-    super.doCall((inner) => inner.ef(ef));
+    this.doVectorCall((inner) => inner.ef(ef));
    return this;
  }

@@ -606,7 +625,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * whose data type is a fixed-size-list of floats.
   */
  column(column: string): VectorQuery {
-    super.doCall((inner) => inner.column(column));
+    this.doVectorCall((inner) => inner.column(column));
    return this;
  }

@@ -627,7 +646,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
  distanceType(
    distanceType: Required<IvfPqOptions>["distanceType"],
  ): VectorQuery {
-    super.doCall((inner) => inner.distanceType(distanceType));
+    this.doVectorCall((inner) => inner.distanceType(distanceType));
    return this;
  }

@@ -661,7 +680,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * distance between the query vector and the actual uncompressed vector.
   */
  refineFactor(refineFactor: number): VectorQuery {
-    super.doCall((inner) => inner.refineFactor(refineFactor));
+    this.doVectorCall((inner) => inner.refineFactor(refineFactor));
    return this;
  }

@@ -686,7 +705,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * factor can often help restore some of the results lost by post filtering.
   */
  postfilter(): VectorQuery {
-    super.doCall((inner) => inner.postfilter());
+    this.doVectorCall((inner) => inner.postfilter());
    return this;
  }

@@ -700,7 +719,7 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   * calculate your recall to select an appropriate value for nprobes.
   */
  bypassVectorIndex(): VectorQuery {
-    super.doCall((inner) => inner.bypassVectorIndex());
+    this.doVectorCall((inner) => inner.bypassVectorIndex());
    return this;
  }

@@ -716,35 +735,31 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
   */
  addQueryVector(vector: IntoVector): VectorQuery {
    if (vector instanceof Promise) {
+      // Observe the promise as soon as it is accepted. The existing native
+      // query may still be pending, and delaying observation until it resolves
+      // can otherwise surface a fast rejection as unhandled.
+      const settledVector = vector.then(
+        (value) => ({ status: "fulfilled" as const, value }),
+        (reason) => ({ status: "rejected" as const, reason }),
+      );
      const res = (async () => {
-        try {
-          const v = await vector;
-          // biome-ignore lint/suspicious/noExplicitAny: we need to get the `inner`, but js has no package scoping
-          const value: any = this.addQueryVector(v);
-          const inner = value.inner as
-            | NativeVectorQuery
-            | Promise<NativeVectorQuery>;
-          return inner;
-        } catch (e) {
-          return Promise.reject(e);
+        const inner = await this.getInner();
+        const outcome = await settledVector;
+        if (outcome.status === "rejected") {
+          throw outcome.reason;
        }
+        addQueryVectorToNative(inner, outcome.value);
+        return inner;
      })();
      return new VectorQuery(res);
    } else {
-      super.doCall((inner) => {
-        const raw = Array.isArray(vector) ? null : extractVectorBuffer(vector);
-        if (raw) {
-          inner.addQueryVectorRaw(raw.data, raw.dtype);
-        } else {
-          inner.addQueryVector(Float32Array.from(vector as number[]));
-        }
-      });
+      this.doVectorCall((inner) => addQueryVectorToNative(inner, vector));
      return this;
    }
  }

  rerank(reranker: Reranker): VectorQuery {
-    super.doCall((inner) =>
+    this.doVectorCall((inner) =>
      inner.rerank(async (args) => {
        const vecResults = await fromBufferToRecordBatch(args.vecResults);
        const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
@@ -763,6 +778,71 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
  }
 }

+/**
+ * Create a string query whose vector/FTS routing is resolved against the active
+ * table schema when the query executes.
+ *
+ * @hidden
+ */
+export function createAutoQuery(
+  table: NativeTable,
+  query: string,
+  columns: string[] | null,
+  getVector: (metadata: string) => Promise<Awaited<IntoVector>>,
+): AutoQuery {
+  type RouteSnapshot = {
+    table: NativeTable;
+    embeddingMetadata: string | undefined;
+  };
+  type CachedPreparation = {
+    metadata: string;
+    vector: Promise<Awaited<IntoVector>>;
+  };
+
+  let cachedPreparation: CachedPreparation | undefined;
+
+  const snapshotRoute = async (): Promise<RouteSnapshot> => {
+    const snapshot = await table.querySnapshot();
+    const schema = tableFromIPC(await snapshot.schema()).schema;
+    return {
+      table: snapshot,
+      embeddingMetadata: schema.metadata.get("embedding_functions"),
+    };
+  };
+
+  const createInner = async (): Promise<NativeQuery | NativeVectorQuery> => {
+    const route = await snapshotRoute();
+    if (route.embeddingMetadata === undefined) {
+      const inner = route.table.query();
+      inner.fullTextSearch({ query, columns });
+      return inner;
+    }
+
+    const metadata = route.embeddingMetadata;
+    if (cachedPreparation?.metadata !== metadata) {
+      cachedPreparation = {
+        metadata,
+        vector: Promise.resolve().then(() => getVector(metadata)),
+      };
+    }
+
+    const preparation = cachedPreparation;
+    let vector: Awaited<IntoVector>;
+    try {
+      vector = await preparation.vector;
+    } catch (error) {
+      if (cachedPreparation === preparation) {
+        cachedPreparation = undefined;
+      }
+      throw error;
+    }
+
+    return nearestToNative(route.table.query(), vector);
+  };
+
+  return new AutoQuery(createInner);
+}
+
 /**
 * A query that returns a subset of the rows in the table.
 *
@@ -788,6 +868,51 @@ export class TakeQuery extends QueryBase<NativeTakeQuery> {
  }
 }

+/**
+ * A builder for automatic string searches.
+ *
+ * Automatic search determines whether to use full-text or vector search from
+ * the table revision selected for each execution. This builder exposes the
+ * common operations supported by both query families.
+ *
+ * @hideconstructor
+ */
+export class AutoQuery extends StandardQueryBase<
+  NativeQuery | NativeVectorQuery
+> {
+  private readonly calls: Array<
+    (inner: NativeQuery | NativeVectorQuery) => void
+  > = [];
+
+  /** @hidden */
+  constructor(
+    private readonly createInner: () => Promise<
+      NativeQuery | NativeVectorQuery
+    >,
+  ) {
+    super();
+  }
+
+  /** @hidden */
+  protected override doCall(
+    fn: (inner: NativeQuery | NativeVectorQuery) => void,
+  ) {
+    this.calls.push(fn);
+  }
+
+  /** @hidden */
+  protected override async getInner(): Promise<
+    NativeQuery | NativeVectorQuery
+  > {
+    const calls = [...this.calls];
+    const inner = await this.createInner();
+    for (const call of calls) {
+      call(inner);
+    }
+    return inner;
+  }
+}
+
 /** A builder for LanceDB queries.
 *
 * @see {@link Table#query}, {@link Table#search}
@@ -840,45 +965,19 @@ export class Query extends StandardQueryBase<NativeQuery> {
   * a default `limit` of 10 will be used.  @see {@link Query#limit}
   */
  nearestTo(vector: IntoVector): VectorQuery {
-    const callNearestTo = (
-      inner: NativeQuery,
-      resolved: Float32Array | Float64Array | Uint8Array | number[],
-    ): NativeVectorQuery => {
-      const raw = Array.isArray(resolved)
-        ? null
-        : extractVectorBuffer(resolved);
-      if (raw) {
-        return inner.nearestToRaw(raw.data, raw.dtype);
-      }
-      return inner.nearestTo(Float32Array.from(resolved as number[]));
-    };
-
-    if (this.inner instanceof Promise) {
-      const nativeQuery = this.inner.then(async (inner) => {
-        const resolved = vector instanceof Promise ? await vector : vector;
-        return callNearestTo(inner, resolved);
-      });
+    const inner = this.inner;
+    if (inner instanceof Promise) {
+      const nativeQuery = inner.then(async (resolvedInner) =>
+        nearestToNative(resolvedInner, await vector),
+      );
      return new VectorQuery(nativeQuery);
    }
    if (vector instanceof Promise) {
-      const res = (async () => {
-        try {
-          const v = await vector;
-          // biome-ignore lint/suspicious/noExplicitAny: we need to get the `inner`, but js has no package scoping
-          const value: any = this.nearestTo(v);
-          const inner = value.inner as
-            | NativeVectorQuery
-            | Promise<NativeVectorQuery>;
-          return inner;
-        } catch (e) {
-          return Promise.reject(e);
-        }
-      })();
-      return new VectorQuery(res);
-    } else {
-      const vectorQuery = callNearestTo(this.inner, vector);
-      return new VectorQuery(vectorQuery);
+      return new VectorQuery(
+        vector.then((resolvedVector) => nearestToNative(inner, resolvedVector)),
+      );
    }
+    return new VectorQuery(nearestToNative(inner, vector));
  }

  nearestToText(query: string | FullTextQuery, columns?: string[]): Query {
@@ -0,0 +1,566 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import {
+  Binary,
+  Bool,
+  DataType,
+  Dictionary,
+  Field,
+  FixedSizeList,
+  Float32,
+  Float64,
+  Int32,
+  Int64,
+  List,
+  Schema,
+  Struct,
+  Utf8,
+  util as arrowUtil,
+} from "apache-arrow";
+import { typedArrayToArrowType } from "./arrow_type";
+import { sanitizeType } from "./sanitize";
+
+type InferenceOptions = {
+  dictionaryEncodeStrings: boolean;
+  vectorColumns: Record<string, { type: unknown }>;
+};
+
+/**
+ * Infer the Arrow schema represented by a set of records.
+ *
+ * This is the intentionally small interface to schema inference. The stateful
+ * details of combining partial type evidence are encapsulated below so callers
+ * only need to provide records, an optional schema, and inference options.
+ */
+export function inferSchema(
+  data: Array<Record<string, unknown>>,
+  schema: Schema | undefined,
+  options: InferenceOptions,
+): Schema {
+  return new SchemaInferrer(schema, options).infer(data);
+}
+
+class SchemaInferrer {
+  private readonly fields = new FieldTree();
+
+  constructor(
+    private readonly providedSchema: Schema | undefined,
+    private readonly options: InferenceOptions,
+  ) {}
+
+  infer(data: Array<Record<string, unknown>>): Schema {
+    for (const [row, record] of data.entries()) {
+      for (const [path, value] of recordPathsAndValues(record)) {
+        this.observe(path, value, row);
+      }
+    }
+
+    return this.providedSchema === undefined
+      ? new Schema(fieldsFromTree(this.fields))
+      : new Schema(matchingFields(this.providedSchema.fields, this.fields));
+  }
+
+  private observe(path: string[], value: unknown, row: number): void {
+    const current = this.fields.get(path);
+    if (current === undefined) {
+      this.addField(path, value, row);
+    } else if (this.providedSchema === undefined) {
+      this.updateInferredField(path, value, row, current);
+    }
+  }
+
+  private addField(path: string[], value: unknown, row: number): void {
+    if (this.providedSchema !== undefined) {
+      this.addSchemaField(this.providedSchema, path, row);
+      return;
+    }
+
+    const evidence =
+      this.inferType(value, path) ?? DeferredTypeEvidence.from(value, row);
+    if (evidence === undefined) {
+      throw typeInferenceError(path, row);
+    }
+
+    const conflict = this.fields.set(
+      path,
+      evidence,
+      (existing) =>
+        existing instanceof DeferredTypeEvidence && existing.isOnlyNulls(),
+    );
+    if (conflict !== undefined) {
+      throw branchConflictError(conflict, row, "Struct");
+    }
+  }
+
+  private addSchemaField(schema: Schema, path: string[], row: number): void {
+    const field = fieldAtPath(schema, path);
+    if (field === undefined) {
+      throw new Error(
+        `Found field not in schema: ${path.join(".")} at row ${row}`,
+      );
+    }
+
+    const conflict = this.fields.set(path, field.type);
+    if (conflict !== undefined) {
+      throw branchConflictError(conflict, row, "Struct");
+    }
+  }
+
+  private updateInferredField(
+    path: string[],
+    value: unknown,
+    row: number,
+    current: FieldNode,
+  ): void {
+    const newType = this.inferType(value, path);
+    const deferred = DeferredTypeEvidence.from(value, row);
+
+    if (current instanceof FieldTree) {
+      if (deferred?.isOnlyNulls()) {
+        return;
+      }
+      throw schemaInferenceError(
+        path,
+        row,
+        "Struct",
+        describeEvidence(newType ?? deferred),
+      );
+    }
+
+    if (current instanceof DeferredTypeEvidence) {
+      this.resolveDeferredField(path, row, current, newType, deferred);
+      return;
+    }
+
+    if (newType !== undefined) {
+      if (!inferredTypesEqual(current, newType)) {
+        throw schemaInferenceError(
+          path,
+          row,
+          describeEvidence(current),
+          describeEvidence(newType),
+        );
+      }
+      return;
+    }
+
+    if (deferred === undefined || !deferred.matches(current)) {
+      throw schemaInferenceError(
+        path,
+        row,
+        describeEvidence(current),
+        describeEvidence(deferred),
+      );
+    }
+  }
+
+  private resolveDeferredField(
+    path: string[],
+    row: number,
+    current: DeferredTypeEvidence,
+    newType: DataType | undefined,
+    deferred: DeferredTypeEvidence | undefined,
+  ): void {
+    if (newType !== undefined) {
+      if (!current.matches(newType)) {
+        throw schemaInferenceError(
+          path,
+          row,
+          current.describe(),
+          describeEvidence(newType),
+        );
+      }
+      this.fields.set(path, newType);
+      return;
+    }
+
+    if (deferred !== undefined) {
+      this.fields.set(path, current.merge(deferred));
+      return;
+    }
+
+    throw schemaInferenceError(
+      path,
+      row,
+      current.describe(),
+      describeEvidence(newType),
+    );
+  }
+
+  private inferType(value: unknown, path: string[]): DataType | undefined {
+    if (typeof value === "bigint") {
+      return new Int64();
+    }
+    if (typeof value === "number") {
+      return new Float64();
+    }
+    if (typeof value === "string") {
+      return this.options.dictionaryEncodeStrings
+        ? new Dictionary(new Utf8(), new Int32())
+        : new Utf8();
+    }
+    if (typeof value === "boolean") {
+      return new Bool();
+    }
+    if (value instanceof Buffer) {
+      return new Binary();
+    }
+    if (ArrayBuffer.isView(value) && !(value instanceof DataView)) {
+      const typedArray = typedArrayToArrowType(value);
+      return typedArray === undefined
+        ? undefined
+        : new FixedSizeList(
+            typedArray.length,
+            new Field("item", typedArray.elementType, true),
+          );
+    }
+    if (!Array.isArray(value) || value.length === 0) {
+      return undefined;
+    }
+
+    const configuredVector =
+      path.length === 1 ? this.options.vectorColumns[path[0]] : undefined;
+    if (configuredVector !== undefined) {
+      return new FixedSizeList(
+        value.length,
+        new Field("item", sanitizeType(configuredVector.type), true),
+      );
+    }
+
+    const itemType = this.inferArrayItemType(value, path);
+    if (itemType === undefined) {
+      return undefined;
+    }
+
+    return nameSuggestsVectorColumn(path[path.length - 1])
+      ? new FixedSizeList(value.length, new Field("item", new Float32(), true))
+      : new List(new Field("item", itemType, true));
+  }
+
+  private inferArrayItemType(
+    values: unknown[],
+    path: string[],
+  ): DataType | undefined {
+    let itemType: DataType | undefined;
+    const deferredItems: unknown[] = [];
+
+    for (const value of values) {
+      const candidate = this.inferType(value, path);
+      if (candidate === undefined) {
+        if (!isDeferredValue(value)) {
+          return undefined;
+        }
+        deferredItems.push(value);
+      } else if (itemType === undefined) {
+        itemType = candidate;
+      } else if (!inferredTypesEqual(itemType, candidate)) {
+        return undefined;
+      }
+    }
+
+    if (itemType === undefined) {
+      return undefined;
+    }
+    return deferredItems.every((value) =>
+      deferredValueMatchesType(value, itemType),
+    )
+      ? itemType
+      : undefined;
+  }
+}
+
+/** Nulls and empty/all-null lists that do not determine a type by themselves. */
+class DeferredTypeEvidence {
+  private constructor(
+    private readonly values: Array<{ value: unknown; row: number }>,
+  ) {}
+
+  static from(value: unknown, row: number): DeferredTypeEvidence | undefined {
+    return isDeferredValue(value)
+      ? new DeferredTypeEvidence([{ value, row }])
+      : undefined;
+  }
+
+  isOnlyNulls(): boolean {
+    return this.values.every(({ value }) => value == null);
+  }
+
+  matches(type: DataType): boolean {
+    return this.values.every(({ value }) =>
+      deferredValueMatchesType(value, type),
+    );
+  }
+
+  merge(other: DeferredTypeEvidence): DeferredTypeEvidence {
+    return new DeferredTypeEvidence([...this.values, ...other.values]);
+  }
+
+  describe(): string {
+    const list = this.values.find(({ value }) => Array.isArray(value));
+    return list === undefined
+      ? "null"
+      : `List[${(list.value as unknown[]).length}]`;
+  }
+
+  firstRow(): number {
+    return this.values[0].row;
+  }
+}
+
+type FieldNode = DataType | DeferredTypeEvidence | FieldTree;
+type LeafNode = Exclude<FieldNode, FieldTree>;
+type FieldConflict = { path: string[]; value: FieldNode };
+
+/** Nested field state, kept separate from Arrow's eventual Struct types. */
+class FieldTree {
+  private readonly children = new Map<string, FieldNode>();
+
+  get(path: string[]): FieldNode | undefined {
+    let current: FieldNode = this;
+    for (const part of path) {
+      if (!(current instanceof FieldTree)) {
+        return undefined;
+      }
+      const child = current.children.get(part);
+      if (child === undefined) {
+        return undefined;
+      }
+      current = child;
+    }
+    return current;
+  }
+
+  set(
+    path: string[],
+    value: LeafNode,
+    canReplaceLeaf: (value: LeafNode) => boolean = () => false,
+  ): FieldConflict | undefined {
+    let branch: FieldTree = this;
+    for (const [index, part] of path.slice(0, -1).entries()) {
+      const child = branch.children.get(part);
+      if (child === undefined || (isLeaf(child) && canReplaceLeaf(child))) {
+        const nextBranch = new FieldTree();
+        branch.children.set(part, nextBranch);
+        branch = nextBranch;
+      } else if (child instanceof FieldTree) {
+        branch = child;
+      } else {
+        return { path: path.slice(0, index + 1), value: child };
+      }
+    }
+
+    const name = path[path.length - 1];
+    const current = branch.children.get(name);
+    if (current instanceof FieldTree) {
+      return { path, value: current };
+    }
+    branch.children.set(name, value);
+    return undefined;
+  }
+
+  entries(): IterableIterator<[string, FieldNode]> {
+    return this.children.entries();
+  }
+
+  has(name: string): boolean {
+    return this.children.has(name);
+  }
+}
+
+function isLeaf(value: FieldNode): value is LeafNode {
+  return !(value instanceof FieldTree);
+}
+
+function fieldsFromTree(tree: FieldTree, path: string[] = []): Field[] {
+  const fields: Field[] = [];
+  for (const [name, value] of tree.entries()) {
+    if (value instanceof FieldTree) {
+      fields.push(
+        new Field(
+          name,
+          new Struct(fieldsFromTree(value, [...path, name])),
+          true,
+        ),
+      );
+    } else if (value instanceof DeferredTypeEvidence) {
+      throw typeInferenceError([...path, name], value.firstRow());
+    } else {
+      fields.push(new Field(name, value, true));
+    }
+  }
+  return fields;
+}
+
+function matchingFields(fields: Field[], tree: FieldTree): Field[] {
+  const matches: Field[] = [];
+  for (const field of fields) {
+    if (!tree.has(field.name)) {
+      continue;
+    }
+    const value = tree.get([field.name]);
+    if (value instanceof FieldTree) {
+      const struct = field.type as Struct;
+      matches.push(
+        new Field(
+          field.name,
+          new Struct(matchingFields(struct.children, value)),
+          field.nullable,
+        ),
+      );
+    } else {
+      matches.push(new Field(field.name, value as DataType, field.nullable));
+    }
+  }
+  return matches;
+}
+
+function* recordPathsAndValues(
+  record: Record<string, unknown>,
+  path: string[] = [],
+): Generator<[string[], unknown]> {
+  for (const [name, value] of Object.entries(record)) {
+    if (isRecord(value)) {
+      yield* recordPathsAndValues(value, [...path, name]);
+    } else if (value !== undefined) {
+      yield [[...path, name], value];
+    }
+  }
+}
+
+function isRecord(value: unknown): value is Record<string, unknown> {
+  return (
+    typeof value === "object" &&
+    value !== null &&
+    !Array.isArray(value) &&
+    !(value instanceof RegExp) &&
+    !(value instanceof Date) &&
+    !(value instanceof Set) &&
+    !(value instanceof Map) &&
+    !(value instanceof Buffer) &&
+    !ArrayBuffer.isView(value)
+  );
+}
+
+function fieldAtPath(schema: Schema, path: string[]): Field | undefined {
+  let fields = schema.fields;
+  let field: Field | undefined;
+  for (const [index, name] of path.entries()) {
+    field = fields.find((candidate) => candidate.name === name);
+    if (field === undefined || index === path.length - 1) {
+      return field;
+    }
+    if (!DataType.isStruct(field.type)) {
+      return undefined;
+    }
+    fields = field.type.children;
+  }
+  return field;
+}
+
+function isDeferredValue(value: unknown): boolean {
+  return (
+    value == null || (Array.isArray(value) && value.every(isDeferredValue))
+  );
+}
+
+function deferredValueMatchesType(value: unknown, type: DataType): boolean {
+  if (value == null) {
+    return true;
+  }
+  if (!Array.isArray(value)) {
+    return false;
+  }
+  if (DataType.isList(type)) {
+    return value.every((item) =>
+      deferredValueMatchesType(item, type.valueType),
+    );
+  }
+  if (DataType.isFixedSizeList(type)) {
+    return (
+      value.length === type.listSize &&
+      value.every((item) => deferredValueMatchesType(item, type.valueType))
+    );
+  }
+  return false;
+}
+
+function inferredTypesEqual(current: DataType, candidate: DataType): boolean {
+  if (DataType.isDictionary(current)) {
+    return (
+      DataType.isDictionary(candidate) &&
+      current.isOrdered === candidate.isOrdered &&
+      inferredTypesEqual(current.indices, candidate.indices) &&
+      inferredTypesEqual(current.dictionary, candidate.dictionary)
+    );
+  }
+  if (DataType.isList(current)) {
+    return (
+      DataType.isList(candidate) &&
+      current.valueField.name === candidate.valueField.name &&
+      current.valueField.nullable === candidate.valueField.nullable &&
+      inferredTypesEqual(current.valueType, candidate.valueType)
+    );
+  }
+  if (DataType.isFixedSizeList(current)) {
+    return (
+      DataType.isFixedSizeList(candidate) &&
+      current.listSize === candidate.listSize &&
+      current.valueField.name === candidate.valueField.name &&
+      current.valueField.nullable === candidate.valueField.nullable &&
+      inferredTypesEqual(current.valueType, candidate.valueType)
+    );
+  }
+  return arrowUtil.compareTypes(current, candidate);
+}
+
+function describeEvidence(
+  evidence: DataType | DeferredTypeEvidence | undefined,
+): string {
+  if (evidence === undefined) {
+    return "an unsupported value";
+  }
+  return evidence instanceof DeferredTypeEvidence
+    ? evidence.describe()
+    : evidence.toString();
+}
+
+function branchConflictError(
+  conflict: FieldConflict,
+  row: number,
+  candidate: string,
+): Error {
+  return schemaInferenceError(
+    conflict.path,
+    row,
+    conflict.value instanceof FieldTree
+      ? "Struct"
+      : describeEvidence(conflict.value),
+    candidate,
+  );
+}
+
+function schemaInferenceError(
+  path: string[],
+  row: number,
+  currentType: string,
+  newType: string,
+): Error {
+  return new Error(
+    `Failed to infer schema for data. Previously inferred type ${currentType} ` +
+      `but found ${newType} for field ${path.join(".")} at row ${row}. ` +
+      "Consider providing an explicit schema.",
+  );
+}
+
+function typeInferenceError(path: string[], row: number): Error {
+  return new Error(
+    `Failed to infer data type for field ${path.join(".")} at row ${row}. ` +
+      "Consider providing an explicit schema.",
+  );
+}
+
+function nameSuggestsVectorColumn(name: string): boolean {
+  const normalized = name.toLowerCase();
+  return normalized.includes("vector") || normalized.includes("embedding");
+}
@@ -43,10 +43,12 @@ import {
  Table as _NativeTable,
 } from "./native";
 import {
+  AutoQuery,
  FullTextQuery,
  Query,
  TakeQuery,
  VectorQuery,
+  createAutoQuery,
  instanceOfFullTextQuery,
 } from "./query";
 import { sanitizeType } from "./sanitize";
@@ -523,7 +525,7 @@ export abstract class Table {
    query: string | IntoVector | MultiVector | FullTextQuery,
    queryType?: string,
    ftsColumns?: string | string[],
-  ): VectorQuery | Query;
+  ): VectorQuery | Query | AutoQuery;
  /**
   * Search the table with a given query vector.
   *
@@ -975,10 +977,11 @@ export class LocalTable extends Table {
    return this.inner.display();
  }

-  private async getEmbeddingFunctions(): Promise<
-    Map<string, EmbeddingFunctionConfig>
-  > {
-    const schema = await this.schema();
+  private async getEmbeddingFunctions(
+    inner: _NativeTable = this.inner,
+  ): Promise<Map<string, EmbeddingFunctionConfig>> {
+    const schemaBuf = await inner.schema();
+    const schema = tableFromIPC(schemaBuf).schema;
    const registry = getRegistry();
    return registry.parseFunctions(schema.metadata);
  }
@@ -1160,7 +1163,7 @@ export class LocalTable extends Table {
    query: string | IntoVector | MultiVector | FullTextQuery,
    queryType: string = "auto",
    ftsColumns?: string | string[],
-  ): VectorQuery | Query {
+  ): VectorQuery | Query | AutoQuery {
    if (typeof query !== "string" && !instanceOfFullTextQuery(query)) {
      if (queryType === "fts") {
        throw new Error("Cannot perform full text search on a vector query");
@@ -1175,14 +1178,28 @@ export class LocalTable extends Table {
      });
    }

-    // The query type is auto or vector
-    // fall back to full text search if no embedding functions are defined and the query is a string
-    if (
-      queryType === "auto" &&
-      (getRegistry().length() === 0 || instanceOfFullTextQuery(query))
-    ) {
-      return this.query().fullTextSearch(query, {
-        columns: ftsColumns,
+    if (queryType === "auto") {
+      if (instanceOfFullTextQuery(query)) {
+        return this.query().fullTextSearch(query, {
+          columns: ftsColumns,
+        });
+      }
+
+      const columns =
+        typeof ftsColumns === "string" ? [ftsColumns] : (ftsColumns ?? null);
+      return createAutoQuery(this.inner, query, columns, async (metadata) => {
+        const functions = await getRegistry().parseFunctions(
+          new Map([["embedding_functions", metadata]]),
+        );
+        // TODO: Support multiple embedding functions
+        const embeddingFunc: EmbeddingFunctionConfig | undefined = functions
+          .values()
+          .next().value;
+        // The route only calls this callback when embedding metadata exists.
+        // parseFunctions either yields a provider or reports malformed metadata.
+        if (!embeddingFunc)
+          throw new Error("Invalid embedding function metadata");
+        return await embeddingFunc.function.computeQueryEmbeddings(query);
      });
    }

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.10",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.10",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.10",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.10",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.10",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",
@@ -1,6 +1,6 @@
 {
  "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.38.0-beta.6",
+  "version": "0.38.0-beta.10",
  "os": [
    "win32"
  ],
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.38.0-beta.6",
+	"version": "0.38.0-beta.10",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",
@@ -1,12 +1,12 @@
 {
  "name": "@lancedb/lancedb",
-  "version": "0.38.0-beta.6",
+  "version": "0.38.0-beta.10",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "@lancedb/lancedb",
-      "version": "0.38.0-beta.6",
+      "version": "0.38.0-beta.10",
      "cpu": [
        "x64",
        "arm64"
@@ -11,7 +11,7 @@
    "ann"
  ],
  "private": false,
-  "version": "0.38.0-beta.6",
+  "version": "0.38.0-beta.10",
  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
@@ -17,6 +17,7 @@ use lancedb::connection::{ConnectBuilder, Connection as LanceDBConnection, conne

 use lance_namespace::models::{
    CreateNamespaceRequest, DescribeNamespaceRequest, DropNamespaceRequest, ListNamespacesRequest,
+    ListTablesRequest,
 };
 use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};

@@ -36,6 +37,12 @@ pub struct ListNamespacesResponse {
    pub page_token: Option<String>,
 }

+#[napi(object)]
+pub struct ListTablesResponse {
+    pub tables: Vec<String>,
+    pub page_token: Option<String>,
+}
+
 #[napi(object)]
 pub struct CreateNamespaceResponse {
    pub properties: Option<HashMap<String, String>>,
@@ -206,6 +213,33 @@ impl Connection {
        op.execute().await.default_error()
    }

+    /// List a page of tables in the database.
+    #[napi(catch_unwind)]
+    pub async fn list_tables(
+        &self,
+        namespace_path: Option<Vec<String>>,
+        page_token: Option<String>,
+        limit: Option<u32>,
+    ) -> napi::Result<ListTablesResponse> {
+        let request = ListTablesRequest {
+            // The root namespace is an empty path, not an absent one: a namespace-backed
+            // database rejects a request that names no namespace.
+            id: Some(namespace_path.unwrap_or_default()),
+            page_token,
+            limit: limit.map(|limit| i32::try_from(limit).unwrap_or(i32::MAX)),
+            ..Default::default()
+        };
+        let response = self
+            .get_inner()?
+            .list_tables(request)
+            .await
+            .default_error()?;
+        Ok(ListTablesResponse {
+            tables: response.tables,
+            page_token: response.page_token,
+        })
+    }
+
    /// Create table from a Apache Arrow IPC (file) buffer.
    ///
    /// Parameters:
@@ -278,6 +278,13 @@ impl Table {
        Ok(Query::new(self.inner_ref()?.query()))
    }

+    /// Return a read-only table handle pinned to the current query revision.
+    #[napi(catch_unwind)]
+    pub async fn query_snapshot(&self) -> napi::Result<Self> {
+        let snapshot = self.inner_ref()?.query_snapshot().await.default_error()?;
+        Ok(Self::new(snapshot))
+    }
+
    #[napi(catch_unwind)]
    pub fn take_offsets(&self, offsets: Vec<i64>) -> napi::Result<TakeQuery> {
        Ok(TakeQuery::new(
@@ -554,6 +561,12 @@ impl Table {
            .default_error()
    }

+    #[napi(catch_unwind)]
+    pub async fn checkout_current(&self) -> napi::Result<Self> {
+        let table = self.inner_ref()?.checkout_current().await.default_error()?;
+        Ok(Self::new(table))
+    }
+
    #[napi(catch_unwind)]
    pub async fn checkout(&self, version: i64) -> napi::Result<()> {
        self.inner_ref()?
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.10"
 publish = false
 edition.workspace = true
 description = "Python bindings for LanceDB"
@@ -101,9 +101,12 @@ azure = ["adlfs>=2024.2.0"]
 [tool.maturin]
 python-source = "python"
 module-name = "lancedb._lancedb"
+# uv installs the project as an editable package before `uv run`, so keep that
+# bootstrap build consistent with `maturin develop`.
+editable-profile = "dev"

 [build-system]
-requires = ["maturin>=1.9.4"]
+requires = ["maturin>=1.10"]
 build-backend = "maturin"

 [tool.ruff.lint]
@@ -179,6 +179,18 @@ def connect(
    ...     },
    ... )

+    For Azure Blob Storage, credentials can be passed directly without setting
+    environment variables:
+
+    >>> azure_storage_options = {
+    ...     "account_name": "some-account",
+    ...     "account_key": "some-key",
+    ... }
+    >>> db = lancedb.connect(  # doctest: +SKIP
+    ...     "az://my-container/my-database",
+    ...     storage_options=azure_storage_options,
+    ... )
+
    For tests and temporary data, use an in-memory database:

    >>> db = lancedb.connect("memory://")
@@ -465,6 +477,10 @@ async def connect_async(
    --------

    >>> import lancedb
+    >>> azure_storage_options = {
+    ...     "account_name": "some-account",
+    ...     "account_key": "some-key",
+    ... }
    >>> async def doctest_example():
    ...     # For a local directory, provide a path to the database
    ...     db = await lancedb.connect_async("~/.lancedb")
@@ -472,6 +488,11 @@ async def connect_async(
    ...     db = await lancedb.connect_async("s3://my-bucket/lancedb",
    ...                                      storage_options={
    ...                                          "aws_access_key_id": "***"})
+    ...     # Azure credentials can also be passed directly
+    ...     db = await lancedb.connect_async(
+    ...         "az://my-container/my-database",
+    ...         storage_options=azure_storage_options,
+    ...     )
    ...     # For tests and temporary data, use an in-memory database
    ...     db = await lancedb.connect_async("memory://")
    ...     # Connect to LanceDB cloud
@@ -283,6 +283,7 @@ class Table:
        mode: Literal["append", "overwrite"],
        progress: Optional[Any] = None,
        write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult: ...
    async def update(
        self, updates: Dict[str, str], where: Optional[str]
@@ -4,7 +4,7 @@
 """Canonical Function values exchanged with LanceDB Enterprise services.

 These immutable models contain client/wire state only. Catalog persistence,
-environment bake, secret resolution, and execution are owned by Sophon.
+environment bake, and execution are owned by Sophon.
 ``RefreshColumnResult`` is also the backend-neutral result of a local
 expression-backed refresh job.
 """
@@ -12,17 +12,19 @@ expression-backed refresh job.
 from __future__ import annotations

 import ast
+import builtins
 import base64
 import functools
 import hashlib
+import importlib
 import inspect
+import symtable
 import json
 import math
 import re
 import sys
 import textwrap
 import types
-import uuid
 from collections.abc import Mapping
 from datetime import date, datetime
 from typing import (
@@ -226,7 +228,7 @@ class PythonEnvironmentSpec(_RemoteValue):


 class PythonRuntimeSpec(_RemoteValue):
-    """Remote runtime definition with non-secret environment values.
+    """Remote runtime definition with environment values.

    V1 supports ``kind="python"``. Newer runtime kinds remain readable, while
    their unknown payload fields are intentionally not retained by the client.
@@ -265,7 +267,6 @@ class FunctionVersion(_RemoteValue):
    runtime: PythonRuntimeSpec
    runtime_digest: str
    environment_digest: str
-    required_secrets: tuple[str, ...] = ()
    created_at: str

    def __call__(self, **inputs: Any) -> FunctionApplication:
@@ -273,7 +274,7 @@ class FunctionVersion(_RemoteValue):

        Every input must be a direct [lancedb.col][lancedb.expr.col]
        reference. The returned application is immutable and retains a
-        named-struct output as one sibling group, so every row's sibling values
+        named-struct output as one binding, so every row's sibling values
        come from one logical Function evaluation. Map result fields to table
        columns with
        [FunctionApplication.rename][lancedb.functions.FunctionApplication.rename],
@@ -323,22 +324,16 @@ class FunctionVersion(_RemoteValue):
            function=FunctionVersionRef(name=self.name, version=self.version),
            inputs=tuple(bindings),
            output=self.signature.output,
-            group_id=f"fg_{uuid.uuid4().hex}",
        )


 class FunctionRegistrationRequest(_RemoteValue):
-    """Stable remote registration envelope produced by :func:`udf`.
-
-    Only secret names are represented. Secret values are resolved inside the
-    remote service and have no client request field.
-    """
+    """Stable remote registration envelope produced by :func:`udf`."""

    name: str
    artifact: FunctionArtifactRequest
    signature: FunctionSignature
    runtime: PythonRuntimeSpec
-    required_secrets: tuple[str, ...] = ()


 class FunctionVersionRef(_OpenRemoteValue):
@@ -367,7 +362,7 @@ class ApplicationInput(_OpenRemoteValue):
 class FunctionApplication(_OpenRemoteValue):
    """Immutable pre-declaration application of an exact Function version.

-    A named-struct output remains one grouped application through table
+    A named-struct output remains one application through table
    declaration and execution.
    [FunctionApplication.rename][lancedb.functions.FunctionApplication.rename]
    records the result-field to table-column mapping without splitting sibling
@@ -377,7 +372,6 @@ class FunctionApplication(_OpenRemoteValue):
    function: FunctionVersionRef
    inputs: tuple[ApplicationInput, ...]
    output: FunctionOutput
-    group_id: str
    columns: Mapping[str, str] = Field(default_factory=dict)

    def _known_dict(self) -> dict[str, Any]:
@@ -449,12 +443,10 @@ class OutputMapping(_RemoteValue):


 class FunctionBinding(_RemoteValue):
-    """Immutable grouped binding persisted by the Enterprise table service."""
+    """Immutable Function binding persisted by the Enterprise table service."""

    binding_id: str
-    revision: _UInt64
    function: FunctionVersionRef
-    group_id: str
    inputs: tuple[InputBinding, ...]
    outputs: tuple[OutputMapping, ...]
    input_schema: Optional[Mapping[str, Any]] = None
@@ -486,62 +478,60 @@ class RefreshColumnResult(_RemoteValue):


 _FUNCTION_NAME = re.compile(r"^[A-Za-z_][A-Za-z0-9_.-]*$")
-_SECRET_NAME = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
+
+
+_GRAMMAR_PRIMITIVES = (
+    (pa.bool_(), "bool"),
+    (pa.int8(), "int8"),
+    (pa.int16(), "int16"),
+    (pa.int32(), "int32"),
+    (pa.int64(), "int64"),
+    (pa.uint8(), "uint8"),
+    (pa.uint16(), "uint16"),
+    (pa.uint32(), "uint32"),
+    (pa.uint64(), "uint64"),
+    (pa.float16(), "float16"),
+    (pa.float32(), "float32"),
+    (pa.float64(), "float64"),
+    (pa.string(), "utf8"),
+    (pa.binary(), "binary"),
+    (pa.date32(), "date32"),
+    (pa.date64(), "date64"),
+)


 def _canonical_arrow_type(data_type: pa.DataType) -> str:
-    primitive_types = (
-        (pa.bool_(), "bool"),
-        (pa.int8(), "int8"),
-        (pa.int16(), "int16"),
-        (pa.int32(), "int32"),
-        (pa.int64(), "int64"),
-        (pa.uint8(), "uint8"),
-        (pa.uint16(), "uint16"),
-        (pa.uint32(), "uint32"),
-        (pa.uint64(), "uint64"),
-        (pa.float16(), "float16"),
-        (pa.float32(), "float32"),
-        (pa.float64(), "float64"),
-        (pa.string(), "utf8"),
-        (pa.large_utf8(), "large_utf8"),
-        (pa.binary(), "binary"),
-        (pa.large_binary(), "large_binary"),
-        (pa.date32(), "date32"),
-        (pa.date64(), "date64"),
-    )
-    for candidate, name in primitive_types:
+    """The server's V1 Function type grammar. Anything outside it is rejected
+    here rather than at registration."""
+    for candidate, name in _GRAMMAR_PRIMITIVES:
        if data_type == candidate:
            return name
-    if pa.types.is_fixed_size_binary(data_type):
-        return f"fixed_size_binary[{data_type.byte_width}]"
-    if pa.types.is_list(data_type):
-        return f"list<{_canonical_arrow_type(data_type.value_type)}>"
-    if pa.types.is_large_list(data_type):
-        return f"large_list<{_canonical_arrow_type(data_type.value_type)}>"
-    if pa.types.is_fixed_size_list(data_type):
+    if pa.types.is_list(data_type) or pa.types.is_large_list(data_type):
+        prefix = "list" if pa.types.is_list(data_type) else "large_list"
+        return f"{prefix}<{_canonical_list_item(data_type)}>"
+    if pa.types.is_fixed_size_list(data_type) and data_type.list_size > 0:
        return (
-            f"fixed_size_list<{_canonical_arrow_type(data_type.value_type)}>"
-            f"[{data_type.list_size}]"
+            f"fixed_size_list<{_canonical_list_item(data_type)}, {data_type.list_size}>"
        )
-    if pa.types.is_struct(data_type):
-        fields = ",".join(
-            f"{field.name}:{_canonical_arrow_type(field.type)}" for field in data_type
-        )
-        return f"struct<{fields}>"
-    if pa.types.is_timestamp(data_type):
-        timezone = f",tz={data_type.tz}" if data_type.tz is not None else ""
-        return f"timestamp[{data_type.unit}{timezone}]"
-    if pa.types.is_time32(data_type) or pa.types.is_time64(data_type):
-        return f"time[{data_type.unit}]"
-    if pa.types.is_duration(data_type):
-        return f"duration[{data_type.unit}]"
-    if pa.types.is_decimal(data_type):
-        bit_width = data_type.bit_width
-        return f"decimal{bit_width}({data_type.precision},{data_type.scale})"
    raise TypeError(f"unsupported Arrow type for Function signature: {data_type}")


+def _canonical_list_item(data_type: pa.DataType) -> str:
+    """The grammar names only the item type; it always means a non-nullable
+    child called `item`, so any other child metadata cannot be represented."""
+    child = data_type.value_field
+    if child.name != "item" or child.nullable or child.metadata:
+        raise TypeError(
+            "unsupported Arrow type for Function signature: list items must be a "
+            f"non-nullable field named 'item', got {child}"
+        )
+    return _canonical_arrow_type(child.type)
+
+
+def _list_of(item: pa.DataType) -> pa.DataType:
+    return pa.list_(pa.field("item", item, nullable=False))
+
+
 def _annotation_type(annotation: Any) -> tuple[pa.DataType, bool]:
    nullable = False
    origin = get_origin(annotation)
@@ -589,7 +579,7 @@ def _annotation_type(annotation: Any) -> tuple[pa.DataType, bool]:
        value_type, value_nullable = _annotation_type(arguments[0])
        if value_nullable:
            raise TypeError("nullable Function list elements are not supported")
-        return pa.list_(value_type), nullable
+        return _list_of(value_type), nullable
    raise TypeError(f"unsupported Function annotation: {annotation!r}")


@@ -736,6 +726,104 @@ def _literal_source(value: Any) -> str:
    )


+_DYNAMIC_NAMESPACE_ACCESS = frozenset(
+    {"globals", "locals", "vars", "eval", "exec", "compile", "__import__"}
+)
+# Modules that hand out namespaces (`sys.modules`, `builtins`, importers,
+# introspection). The artifact's module namespace holds only the names it was
+# packaged with, so reaching around it cannot be represented.
+_NAMESPACE_MODULES = frozenset(
+    {"sys", "builtins", "importlib", "inspect", "gc", "ctypes", "types"}
+)
+
+
+def _namespace_acquisition(
+    definition: ast.FunctionDef, references: set[str]
+) -> list[str]:
+    found = set(references & _DYNAMIC_NAMESPACE_ACCESS)
+    for node in ast.walk(definition):
+        if isinstance(node, ast.Import):
+            found.update(
+                alias.name
+                for alias in node.names
+                if alias.name.split(".")[0] in _NAMESPACE_MODULES
+            )
+        elif isinstance(node, ast.ImportFrom) and node.module:
+            if node.module.split(".")[0] in _NAMESPACE_MODULES:
+                found.add(node.module)
+    return sorted(found)
+
+
+def _module_references(module_source: str) -> set[str]:
+    """Names any scope in `module_source` binds or loads at module scope.
+    Python's own scope analysis on the exact text that ships: free variables
+    belong to an enclosing scope inside the function, and postponed
+    annotations are not runtime loads."""
+
+    def visit(table: symtable.SymbolTable, found: set[str]) -> None:
+        for symbol in table.get_symbols():
+            if symbol.is_global() and (
+                symbol.is_referenced() or symbol.is_declared_global()
+            ):
+                found.add(symbol.get_name())
+        for child in table.get_children():
+            visit(child, found)
+
+    found: set[str] = set()
+    for table in symtable.symtable(module_source, "<udf>", "exec").get_children():
+        visit(table, found)
+    return found
+
+
+def _global_source(name: str, value: Any) -> str:
+    """One module-level line that rebinds `name` to `value` in the artifact:
+    an import for modules and importable classes/functions, a literal otherwise."""
+    if isinstance(value, types.ModuleType):
+        if value.__name__.split(".")[0] in _NAMESPACE_MODULES:
+            raise ValueError(
+                f"@udf cannot package dynamic namespace access: {value.__name__!r}"
+            )
+        try:
+            imported = importlib.import_module(value.__name__)
+        except ImportError:
+            imported = None
+        if imported is not value:
+            raise TypeError(
+                f"Function source references module {name!r} that does not import "
+                f"as {value.__name__!r}"
+            )
+        return f"import {value.__name__} as {name}"
+    module_name = getattr(value, "__module__", None)
+    qualname = getattr(value, "__qualname__", None)
+    if (
+        isinstance(module_name, str)
+        and isinstance(qualname, str)
+        and module_name != "__main__"
+        and "." not in qualname
+        and "<" not in qualname
+    ):
+        try:
+            imported = getattr(importlib.import_module(module_name), qualname)
+        except (ImportError, AttributeError):
+            imported = None
+        if imported is value:
+            return f"from {module_name} import {qualname} as {name}"
+    return f"{name} = {_literal_source(value)}"
+
+
+def _is_recursive_reference(function: Callable[..., Any], name: str) -> bool:
+    """`name` inside the body means the function itself unless the module has
+    since bound it to something else."""
+    if name != function.__name__:
+        return False
+    bound = function.__globals__.get(name, function)
+    if bound is function:
+        return True
+    # The decorator's own result is the one wrapper known to call `function`
+    # unchanged; any other binding may behave differently from a self-call.
+    return type(bound) is UdfDefinition and bound._function is function
+
+
 def _package_source(function: Callable[..., Any]) -> bytes:
    if not inspect.isfunction(function) or inspect.iscoroutinefunction(function):
        raise TypeError("@udf requires a synchronous Python function")
@@ -760,23 +848,46 @@ def _package_source(function: Callable[..., Any]) -> bytes:
    closure = inspect.getclosurevars(function)
    if closure.nonlocals:
        raise ValueError("@udf cannot package functions that capture closure values")
-    if closure.unbound:
-        raise ValueError(
-            f"@udf source contains unresolved global names: {sorted(closure.unbound)!r}"
-        )
-    globals_source = []
-    for name, value in sorted(closure.globals.items()):
-        if isinstance(value, types.ModuleType):
-            globals_source.append(f"import {value.__name__} as {name}")
-        else:
-            globals_source.append(f"{name} = {_literal_source(value)}")
-
    function_source = ast.unparse(definition)
-    parts = ["from __future__ import annotations"]
+    module_header = "from __future__ import annotations"
+    references = _module_references(f"{module_header}\n\n{function_source}\n")
+    dynamic = _namespace_acquisition(definition, references)
+    if dynamic:
+        raise ValueError(f"@udf cannot package dynamic namespace access: {dynamic!r}")
+    # Resolve every module-scope reference the way the interpreter would: the
+    # function's own globals first (a module global may shadow a builtin, and
+    # nested scopes are not visible to getclosurevars), then its builtins.
+    # The artifact runs under the standard builtins; only the exact mapping is
+    # provably equivalent (a subclass or copy can change lookups and hooks).
+    if function.__builtins__ is not vars(builtins):
+        raise ValueError("@udf cannot package a non-standard builtins environment")
+    globals_source = []
+    unresolved = []
+    for name in sorted(references):
+        if name == function.__name__:
+            if not _is_recursive_reference(function, name):
+                raise ValueError(
+                    f"@udf cannot package {name!r}: the module binds that name to "
+                    "another value, which the artifact's own definition would shadow"
+                )
+            continue
+        if name in function.__globals__:
+            globals_source.append(_global_source(name, function.__globals__[name]))
+        elif hasattr(builtins, name):
+            pass
+        else:
+            unresolved.append(name)
+    if unresolved:
+        raise ValueError(
+            f"@udf source contains unresolved global names: {unresolved!r}"
+        )
+
+    parts = [module_header]
    if globals_source:
        parts.extend(["", *globals_source])
    parts.extend(["", function_source, ""])
-    return "\n".join(parts).encode("utf-8")
+    packaged = "\n".join(parts)
+    return packaged.encode("utf-8")


 class UdfDefinition:
@@ -797,7 +908,6 @@ class UdfDefinition:
        output_schema: Optional[pa.DataType | pa.Field | pa.Schema],
        pip: tuple[str, ...],
        env: Mapping[str, str],
-        secrets: tuple[str, ...],
        python_version: Optional[str],
    ):
        function_name = name or function.__name__
@@ -812,18 +922,6 @@ class UdfDefinition:
            for key, value in environment.items()
        ):
            raise TypeError("Function env keys and values must be strings")
-        required_secrets = tuple(sorted(set(secrets)))
-        invalid_secrets = [
-            secret for secret in required_secrets if not _SECRET_NAME.fullmatch(secret)
-        ]
-        if invalid_secrets:
-            raise ValueError(f"invalid Function secret names: {invalid_secrets!r}")
-        overlap = set(environment) & set(required_secrets)
-        if overlap:
-            raise ValueError(
-                f"Function env and secret names must be disjoint: {sorted(overlap)!r}"
-            )
-
        signature = _infer_signature(function, input_schema, output_schema)
        source = _package_source(function)
        digest = f"sha256:{hashlib.sha256(source).hexdigest()}"
@@ -852,7 +950,6 @@ class UdfDefinition:
            ),
            signature=signature,
            runtime=runtime,
-            required_secrets=required_secrets,
        )
        functools.update_wrapper(self, function)

@@ -878,7 +975,6 @@ def udf(
    output_schema: Optional[pa.DataType | pa.Field | pa.Schema] = None,
    pip: tuple[str, ...] | list[str] = (),
    env: Optional[Mapping[str, str]] = None,
-    secrets: tuple[str, ...] | list[str] = (),
    python_version: Optional[str] = None,
 ) -> Callable[[Callable[..., Any]], UdfDefinition]: ...

@@ -891,7 +987,6 @@ def udf(
    output_schema: Optional[pa.DataType | pa.Field | pa.Schema] = None,
    pip: tuple[str, ...] | list[str] = (),
    env: Optional[Mapping[str, str]] = None,
-    secrets: tuple[str, ...] | list[str] = (),
    python_version: Optional[str] = None,
 ):
    """Prepare a scalar Python callable for remote Function registration.
@@ -916,13 +1011,17 @@ def udf(
    pip : sequence of str, optional
        Pip requirements for the remote environment.
    env : mapping of str to str, optional
-        Non-secret environment variables. Use ``secrets`` for credentials.
-    secrets : sequence of str, optional
-        Names of secrets resolved by the remote service. Secret values are not
-        accepted by this API or included in the registration request.
+        Environment variables included in the Function definition.
    python_version : str, optional
        Remote Python major/minor version. Defaults to the client version.

+    The packaged artifact is a snapshot: the function source plus exactly
+    the module-level names it references (modules as imports, importable
+    classes and functions as imports, literals inline). Code that reaches the
+    module namespace another way -- ``globals()``/``eval``, ``sys.modules``,
+    ``builtins`` -- is rejected where it can be seen and otherwise
+    unsupported; closures and a non-standard ``__builtins__`` are rejected.
+
    Returns
    -------
    UdfDefinition
@@ -934,7 +1033,7 @@ def udf(
    Examples
    --------
    >>> from lancedb import udf
-    >>> @udf(pip=["numpy==2.2.0"], secrets=["MODEL_TOKEN"])
+    >>> @udf(pip=["numpy==2.2.0"])
    ... def score(value: float) -> float:
    ...     return value * 2
    >>> score(1.5)
@@ -949,7 +1048,6 @@ def udf(
            output_schema=output_schema,
            pip=tuple(pip),
            env={} if env is None else env,
-            secrets=tuple(secrets),
            python_version=python_version,
        )

@@ -391,6 +391,15 @@ def _table_to_pickle_state(table: Table) -> dict[str, Any]:
    }


+def _drop_base_version(permutation_data: pa.Table) -> pa.Table:
+    """Strip the recorded base version so the reader leaves the base table unpinned."""
+    metadata = dict(permutation_data.schema.metadata or {})
+    if metadata.pop(b"base_version", None) is None:
+        return permutation_data
+    metadata.pop(b"base_branch", None)
+    return permutation_data.replace_schema_metadata(metadata)
+
+
 def _table_from_pickle_state(state: dict[str, Any]) -> Table:
    from . import connect

@@ -679,11 +688,15 @@ class Permutation:
        from . import connect

        connection_factory = state["connection_factory"]
+        rebuilt_base = False
        if connection_factory is not None:
            base_table = connection_factory(state["base_table_name"])
        elif "base_table_state" in state:
-            base_table = _table_from_pickle_state(state["base_table_state"])
+            base_state = state["base_table_state"]
+            rebuilt_base = base_state["kind"] == "memory"
+            base_table = _table_from_pickle_state(base_state)
        elif "base_table_data" in state:
+            rebuilt_base = True
            # In-memory base table inlined into the pickle; rebuild the same
            # way we rebuild the in-memory permutation table.
            mem_db = connect("memory://")
@@ -701,11 +714,14 @@ class Permutation:
            )

        permutation_table: Optional[Table] = None
-        if state["permutation_data"] is not None:
+        permutation_data = state["permutation_data"]
+        if permutation_data is not None:
+            if rebuilt_base:
+                # The base table was materialized from Arrow, so it is a fresh
+                # single-version dataset and the recorded pin cannot resolve on it.
+                permutation_data = _drop_base_version(permutation_data)
            mem_db = connect("memory://")
-            permutation_table = mem_db.create_table(
-                "permutation", state["permutation_data"]
-            )
+            permutation_table = mem_db.create_table("permutation", permutation_data)

        self.base_table = base_table
        self.permutation_table = permutation_table
@@ -610,6 +610,7 @@ class RemoteTable(Table):
        fill_value: float = 0.0,
        progress: Optional[Union[bool, Callable, Any]] = None,
        write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult:
        """Add more data to the [Table][lancedb.table.Table].

@@ -642,6 +643,8 @@ class RemoteTable(Table):
            data in flight. Defaults to an estimate based on the data size,
            capped at the number of CPU cores. Lower this if bulk ingestion is
            using too much memory.
+        allow_external_blob_outside_bases: bool, default False
+            Not supported on LanceDB Cloud. Setting this raises.

        Returns
        -------
@@ -658,6 +661,7 @@ class RemoteTable(Table):
                    fill_value=fill_value,
                    progress=progress,
                    write_parallelism=write_parallelism,
+                    allow_external_blob_outside_bases=allow_external_blob_outside_bases,
                )
            )
        finally:
@@ -19,6 +19,7 @@ above.
 """

 import ctypes
+import heapq
 import logging
 import os
 import random
@@ -29,17 +30,18 @@ from collections import deque
 from concurrent.futures import ThreadPoolExecutor
 from copy import deepcopy
 from multiprocessing import RawArray
-from typing import Any, Callable, cast, Iterator, Literal, Optional, Union
+from typing import Any, Callable, cast, Iterator, Literal, NamedTuple, Optional, Union

 import pyarrow as pa
 import pyarrow.compute as pc
 import torch
-from torch.utils.data import IterableDataset, get_worker_info
+from torch.utils.data import DataLoader, IterableDataset, get_worker_info

 from .permutation import (
    Permutation,
    Transforms,
    permutation_builder,
+    _drop_base_version,
    _table_from_pickle_state,
    _table_to_pickle_state,
 )
@@ -55,6 +57,155 @@ DEFAULT_READ_BATCH_SIZE = 64
 DEFAULT_PREFETCH_BATCHES = 4


+class _WorkerSample(NamedTuple):
+    data: Any
+    dataset: "StreamingDataset"
+
+
+class _WorkerBatch(NamedTuple):
+    data: Any
+    state: dict
+
+
+class _ConsumerIteratorLease(NamedTuple):
+    owner_token: int
+    owner_thread: int
+
+
+class _CheckpointCollate:
+    """Attach the worker's post-fetch state to a collated batch."""
+
+    def __init__(self, collate_fn: Callable):
+        self._collate_fn = collate_fn
+
+    def __call__(self, samples):
+        try:
+            if isinstance(samples, list):
+                if not samples:
+                    return _WorkerBatch(self._collate_fn(samples), {})
+                worker_samples = samples
+                data = self._collate_fn([sample.data for sample in worker_samples])
+                dataset = worker_samples[-1].dataset
+            else:
+                data = self._collate_fn(samples.data)
+                dataset = samples.dataset
+        except StopIteration as exc:
+            raise RuntimeError(
+                "collate_fn raised StopIteration before returning a batch"
+            ) from exc
+        return _WorkerBatch(data, dataset._checkpoint_snapshot())
+
+
+class _StreamingDatasetAdapter(IterableDataset):
+    """Yield private sample wrappers for :class:`StreamingDataLoader`."""
+
+    def __init__(self, dataset: "StreamingDataset"):
+        super().__init__()
+        self.dataset = dataset
+
+    def __iter__(self):
+        for sample in self.dataset._iter(consumer_checkpoint_transport=True):
+            yield _WorkerSample(sample, self.dataset)
+
+    def __getattr__(self, name):
+        dataset = self.__dict__.get("dataset")
+        if dataset is None:
+            raise AttributeError(name)
+        return getattr(dataset, name)
+
+
+class _ConsumerCommitIterator:
+    def __init__(
+        self,
+        iterator,
+        dataset: "StreamingDataset",
+        *,
+        owner_token: int,
+        require_uniform: bool,
+    ):
+        self._iterator = iterator
+        self._dataset = dataset
+        self._owner_token = owner_token
+        self._require_uniform = require_uniform
+        self._released = False
+        self._terminal = False
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self._terminal:
+            raise StopIteration
+        try:
+            batch = next(self._iterator)
+        except StopIteration:
+            self._terminal = True
+            self._release()
+            raise
+        except BaseException as exc:
+            self._dataset._invalidate_checkpoint(
+                f"a DataLoader batch failed before it was returned: {exc}"
+            )
+            raise
+        try:
+            if not isinstance(batch, _WorkerBatch):
+                raise RuntimeError(
+                    "StreamingDataLoader did not receive worker checkpoint metadata"
+                )
+            self._dataset._commit_worker_state(
+                batch.state, require_uniform=self._require_uniform
+            )
+            return batch.data
+        except BaseException as exc:
+            self._dataset._invalidate_checkpoint(
+                f"a DataLoader batch failed before it was returned: {exc}"
+            )
+            raise
+
+    def _release(self) -> None:
+        if self.__dict__.get("_released", True):
+            return
+        self._released = True
+        dataset = self.__dict__.get("_dataset")
+        if dataset is not None:
+            dataset._release_consumer_iterator(self._owner_token)
+
+    def _shutdown_workers(self):
+        if self.__dict__.get("_released", True):
+            return None
+        self._terminal = True
+        iterator = self.__dict__.get("_iterator")
+        shutdown = getattr(iterator, "_shutdown_workers", None)
+        try:
+            if shutdown is not None:
+                shutdown()
+            else:
+                fetcher = getattr(iterator, "_dataset_fetcher", None)
+                dataset_iterator = getattr(fetcher, "dataset_iter", None)
+                close = getattr(dataset_iterator, "close", None)
+                if close is None:
+                    raise RuntimeError(
+                        "StreamingDataLoader could not close its inner iterator"
+                    )
+                close()
+        except BaseException as exc:
+            self._dataset._invalidate_checkpoint(
+                f"a DataLoader iterator could not be shut down safely: {exc}"
+            )
+            raise
+        else:
+            self._release()
+
+    def __del__(self):
+        try:
+            self._shutdown_workers()
+        except BaseException:
+            pass
+
+    def __getattr__(self, name):
+        return getattr(self._iterator, name)
+
+
 class StreamingDataset(IterableDataset):
    """An elastic, resumable PyTorch IterableDataset backed by a LanceDB table.

@@ -384,6 +535,22 @@ class StreamingDataset(IterableDataset):
        #          rows_skipped]
        self._worker_stats: RawArray = RawArray(ctypes.c_int64, 8)

+        # A standard multi-process DataLoader cannot report which prefetched
+        # batches were actually returned to its consumer.  Workers set this
+        # shared flag so state_dict() can reject a stale parent checkpoint
+        # unless StreamingDataLoader installed the consumer-commit transport.
+        self._untracked_worker_iteration: RawArray = RawArray(ctypes.c_int64, 1)
+
+        # Parent-side checkpoint lifecycle.  A failed DataLoader task creates
+        # a permanent hole in that iterator's delivery stream, while a
+        # multi-worker checkpoint is safe to restore only after all splits
+        # reach the same logical step boundary.
+        self._checkpoint_invalid_reason: Optional[str] = None
+        self._consumer_checkpoint_requires_uniform = False
+        self._consumer_iterator_lock = threading.Lock()
+        self._consumer_iterator_generation = 0
+        self._consumer_iterator_lease: Optional[_ConsumerIteratorLease] = None
+
        # Cumulative bytes of Arrow buffer data fetched across all iterations.
        self._bytes_loaded: int = 0
        # Cumulative seconds spent in LanceDB I/O and in transform functions.
@@ -396,6 +563,10 @@ class StreamingDataset(IterableDataset):
        # step boundaries all splits have consumed this many samples, so a
        # single scalar captures the topology-independent checkpoint state.
        self._resume_offset: int = 0
+        # Exact yielded-sample counts for splits this process has advanced.
+        # Missing entries use _resume_offset, which remains the lower-bound
+        # checkpoint inherited from an earlier uniform/global state.
+        self._resume_samples: dict[int, int] = {}
        # Permutation position each split has consumed through, keyed by
        # global split index.  Equal to _resume_offset for every split unless
        # on_transform_error skipped rows, in which case skipped positions
@@ -521,11 +692,45 @@ class StreamingDataset(IterableDataset):
        return self._rank_splits[start : start + splits_per_worker]

    def __iter__(self) -> Iterator[dict[str, Any]]:
+        return self._iter()
+
+    def _iter(
+        self, *, consumer_checkpoint_transport: bool = False
+    ) -> Iterator[dict[str, Any]]:
+        owner_token = None
+        previous_lease = self._consumer_iterator_lease
+        if consumer_checkpoint_transport:
+            if not self._consumer_iterator_active:
+                raise RuntimeError(
+                    "StreamingDataLoader worker transport requires an active "
+                    "parent iterator reservation"
+                )
+        else:
+            try:
+                owner_token = self._acquire_consumer_iterator()
+            except BaseException:
+                self._release_consumer_iterator_after_failed_acquire(previous_lease)
+                raise
+        try:
+            yield from self._iter_owned(
+                consumer_checkpoint_transport=consumer_checkpoint_transport
+            )
+        finally:
+            if owner_token is not None:
+                self._release_consumer_iterator(owner_token)
+
+    def _iter_owned(
+        self, *, consumer_checkpoint_transport: bool
+    ) -> Iterator[dict[str, Any]]:
        if self._raw_batches_ref is not None:
            raise RuntimeError(
                "StreamingDataset does not support concurrent iteration. "
                "Only one active iterator per dataset instance is allowed."
            )
+        real_worker = get_worker_info() is not None
+        if real_worker and not consumer_checkpoint_transport:
+            self._untracked_worker_iteration[0] = 1
+
        my_splits = self._resolve_my_splits()
        if not my_splits:
            return
@@ -533,6 +738,7 @@ class StreamingDataset(IterableDataset):
        # Set identity transform on each Permutation so __getitems__ returns
        # the raw RecordBatch.  Stage 2 applies the real transform.
        permutations: list[Permutation] = []
+        initial_samples: list[int] = []
        initial_positions: list[int] = []
        for split_idx in my_splits:
            perm = Permutation.from_tables(
@@ -541,21 +747,22 @@ class StreamingDataset(IterableDataset):
            if self._columns is not None:
                perm = perm.select_columns(self._columns)
            perm = perm.with_transform(Transforms.arrow2arrow)
+            sample_count = self._resume_samples.get(split_idx, self._resume_offset)
            # Both modes resume from absolute permutation positions. Packing
            # stores them separately because it also checkpoints partial blocks.
            start_pos = (
                self._pack_consumed[split_idx]
                if self._pack_sequences is not None
-                else self._resume_positions.get(split_idx, self._resume_offset)
+                else self._resume_positions.get(split_idx, sample_count)
            )
            if start_pos > 0:
                perm = perm.with_skip(start_pos)
+            initial_samples.append(sample_count)
            initial_positions.append(start_pos)
            permutations.append(perm)

        n = len(permutations)
        split_sizes = [perm.num_rows for perm in permutations]
-        initial_offset = self._resume_offset
        local_consumed = [0] * n
        # Permutation position each split has consumed through (absolute,
        # i.e. counted from the start of the unskipped split).  Runs ahead of
@@ -853,6 +1060,27 @@ class StreamingDataset(IterableDataset):
                    for i in range(n):
                        _fill_io(i)

+                    def _yield_row(i: int):
+                        pos, row = cooked[i].popleft()
+                        # Surface any completed prefetched failure before the
+                        # current row becomes durable checkpoint progress.
+                        _advance(i)
+                        local_consumed[i] += 1
+                        pos_consumed[i] = pos + 1
+                        split_idx = my_splits[i]
+                        self._resume_samples[split_idx] = (
+                            initial_samples[i] + local_consumed[i]
+                        )
+                        self._resume_positions[split_idx] = pos_consumed[i]
+                        return row
+
+                    def _update_progress_stats() -> None:
+                        if not real_worker:
+                            self._resume_offset = min(
+                                initial_samples[j] + local_consumed[j] for j in range(n)
+                            )
+                        _update_stats()
+
                    if self._pack_sequences is not None:
                        first_count = pack_blocks_emitted[my_splits[0]]
                        if any(
@@ -878,12 +1106,38 @@ class StreamingDataset(IterableDataset):
                                    tokens.extend([pad_id] * (pack_len - len(tokens)))
                                block = _emit_block(i)
                                pack_blocks_emitted[my_splits[i]] += 1
+                                # Checkpoint state must advance before yielding so
+                                # StreamingDataLoader can attach the exact state to
+                                # the batch it transports to the parent process.
+                                _commit_pack_state()
                                if i == n - 1:
-                                    _commit_pack_state()
                                    _update_stats()
                                yield block
                        return

+                    # A checkpoint taken between round-robin split turns has
+                    # non-uniform counts.  Resume lagging splits first so the
+                    # exact canonical sequence continues without replaying
+                    # already-consumed rows.
+                    if len(set(initial_samples)) > 1:
+                        catch_up_to = max(initial_samples)
+                        pending = [
+                            (initial_samples[i], my_splits[i], i)
+                            for i in range(n)
+                            if initial_samples[i] < catch_up_to
+                        ]
+                        heapq.heapify(pending)
+                        while pending:
+                            consumed, _, i = heapq.heappop(pending)
+                            _ensure_cooked(i)
+                            if not cooked[i]:
+                                return
+                            row = _yield_row(i)
+                            if consumed + 1 < catch_up_to:
+                                heapq.heappush(pending, (consumed + 1, my_splits[i], i))
+                            _update_progress_stats()
+                            yield row
+
                    while True:
                        # A cycle only runs if every split can still produce a
                        # row.  Without skips all splits exhaust simultaneously
@@ -904,20 +1158,14 @@ class StreamingDataset(IterableDataset):
                            break

                        for i in range(n):
-                            pos, row = cooked[i].popleft()
-                            local_consumed[i] += 1
-                            pos_consumed[i] = pos + 1
-                            _advance(i)
+                            row = _yield_row(i)

                            # After the last split in each cycle: update the
                            # global offset and refresh the shared-memory stats
                            # so the main process can observe pipeline depth
                            # even when __iter__ runs in a worker process.
                            if i == n - 1:
-                                self._resume_offset = initial_offset + local_consumed[i]
-                                for j, split_idx in enumerate(my_splits):
-                                    self._resume_positions[split_idx] = pos_consumed[j]
-                                _update_stats()
+                                _update_progress_stats()

                            yield row
                finally:
@@ -1064,6 +1312,7 @@ class StreamingDataset(IterableDataset):
            "_local_consumed_ref",
        ):
            state[key] = None
+        state["_consumer_iterator_lock"] = None
        return state

    def __setstate__(self, state):
@@ -1074,19 +1323,31 @@ class StreamingDataset(IterableDataset):
        table_state = state.pop("_table")
        perm_name, perm_data = state.pop("_perm_table")
        self.__dict__.update(state)
+        self._consumer_iterator_lock = threading.Lock()
        if self._connection_factory is not None:
            self._table = self._connection_factory(table_name)
        else:
            self._table = _table_from_pickle_state(table_state)
+            if table_state["kind"] == "memory":
+                # Rebuilt from Arrow, so the recorded pin cannot resolve on it.
+                perm_data = _drop_base_version(perm_data)
        self._perm_table = _connect("memory://").create_table(perm_name, perm_data)

    def state_dict(self) -> dict:
        """Snapshot the dataset's consumption state.

+        When using DataLoader workers, construct a
+        [StreamingDataLoader][lancedb.streaming.StreamingDataLoader].  It
+        commits worker state only when a prefetched batch is returned to the
+        trainer.  A standard multi-process ``DataLoader`` cannot expose that
+        boundary, so calling this method after one has started raises
+        ``RuntimeError`` instead of returning stale producer state.
+
        In row mode, the returned dict is topology-independent at global step
        boundaries. ``positions_consumed_per_split`` records how far each
        split's permutation has advanced, which can differ from the sample
-        count when ``on_transform_error`` skips rows. Combine state dicts from
+        count when ``on_transform_error`` skips rows. ``StreamingDataLoader``
+        combines worker state in its parent process. Combine state dicts from
        every rank with
        [merge_state_dicts][lancedb.streaming.StreamingDataset.merge_state_dicts]
        before resuming on a different topology.
@@ -1095,6 +1356,43 @@ class StreamingDataset(IterableDataset):
        for every logical split. When packing is sharded, merge every rank
        state with ``merge_state_dicts`` before loading it.
        """
+        if self._untracked_worker_iteration[0] and get_worker_info() is None:
+            raise RuntimeError(
+                "StreamingDataset cannot checkpoint a standard DataLoader with "
+                "num_workers > 0 because prefetched worker progress is not "
+                "consumer-committed. Use StreamingDataLoader instead."
+            )
+        if self._checkpoint_invalid_reason is not None:
+            raise RuntimeError(
+                "StreamingDataset checkpointing is invalid because "
+                f"{self._checkpoint_invalid_reason}. Load the last valid "
+                "checkpoint into a fresh dataset before continuing."
+            )
+        state = self._checkpoint_snapshot()
+        if self._pack_sequences is not None:
+            rank_blocks = [
+                state["blocks_emitted_per_split"][split] for split in self._rank_splits
+            ]
+            if len(set(rank_blocks)) > 1:
+                raise RuntimeError(
+                    "Packed StreamingDataset checkpointing is only safe at a "
+                    "complete logical step boundary, when every split assigned "
+                    "to this rank has emitted the same block count. Consume more "
+                    "batches before calling state_dict()."
+                )
+        elif self._consumer_checkpoint_requires_uniform:
+            samples = state["samples_consumed_per_split"]
+            rank_samples = [samples[split] for split in self._rank_splits]
+            if len(set(rank_samples)) > 1:
+                raise RuntimeError(
+                    "StreamingDataLoader checkpointing with multiple workers is "
+                    "only safe at a complete logical step boundary, when every "
+                    "split assigned to this rank has the same consumed-sample "
+                    "count. Consume more batches before calling state_dict()."
+                )
+        return state
+
+    def _checkpoint_snapshot(self) -> dict:
        if self._pack_sequences is not None:
            return {
                "shuffle_seed": self._shuffle_seed,
@@ -1108,18 +1406,141 @@ class StreamingDataset(IterableDataset):
                "blocks_emitted_per_split": list(self._pack_blocks_emitted),
                "pack_buffers": deepcopy(self._pack_buffers),
            }
+        samples = [
+            self._resume_samples.get(split, self._resume_offset)
+            for split in range(self._num_splits)
+        ]
        positions = [
-            self._resume_positions.get(split, self._resume_offset)
+            self._resume_positions.get(split, samples[split])
            for split in range(self._num_splits)
        ]
        return {
            "shuffle_seed": self._shuffle_seed,
            "num_splits": self._num_splits,
            "epoch": self._epoch,
-            "samples_consumed_per_split": [self._resume_offset] * self._num_splits,
+            "samples_consumed_per_split": samples,
            "positions_consumed_per_split": positions,
        }

+    def _invalidate_checkpoint(self, reason: str) -> None:
+        if self._checkpoint_invalid_reason is None:
+            self._checkpoint_invalid_reason = reason
+
+    @property
+    def _consumer_iterator_active(self) -> bool:
+        return self._consumer_iterator_lease is not None
+
+    @property
+    def _consumer_iterator_owner(self) -> Optional[int]:
+        lease = self._consumer_iterator_lease
+        return lease.owner_token if lease is not None else None
+
+    @property
+    def _consumer_iterator_owner_thread(self) -> Optional[int]:
+        lease = self._consumer_iterator_lease
+        return lease.owner_thread if lease is not None else None
+
+    def _acquire_consumer_iterator(self) -> int:
+        """Reserve this parent dataset for one checkpoint-aware iterator."""
+        with self._consumer_iterator_lock:
+            if self._consumer_iterator_active or self._raw_batches_ref is not None:
+                raise RuntimeError(
+                    "StreamingDataset does not support concurrent iteration. "
+                    "Only one active iterator per dataset instance is allowed."
+                )
+            owner_thread = threading.get_ident()
+            owner_token = self._consumer_iterator_generation + 1
+            lease = _ConsumerIteratorLease(owner_token, owner_thread)
+            self._consumer_iterator_generation = owner_token
+            self._consumer_iterator_lease = lease
+            return owner_token
+
+    def _release_consumer_iterator(self, owner_token: int) -> None:
+        with self._consumer_iterator_lock:
+            lease = self._consumer_iterator_lease
+            if lease is not None and lease.owner_token == owner_token:
+                self._consumer_iterator_lease = None
+
+    def _release_consumer_iterator_after_failed_acquire(
+        self, previous_lease: Optional[_ConsumerIteratorLease]
+    ) -> None:
+        """Clean up when an interrupted acquire set a lease but did not return it."""
+        owner_thread = threading.current_thread().ident
+        with self._consumer_iterator_lock:
+            lease = self._consumer_iterator_lease
+            if (
+                lease is not None
+                and lease is not previous_lease
+                and lease.owner_thread == owner_thread
+            ):
+                self._consumer_iterator_lease = None
+
+    def _commit_worker_state(self, state: dict, *, require_uniform: bool) -> None:
+        """Merge one trainer-consumed worker batch into parent state."""
+        for key, expected in (
+            ("shuffle_seed", self._shuffle_seed),
+            ("num_splits", self._num_splits),
+            ("epoch", self._epoch),
+        ):
+            if state.get(key) != expected:
+                raise ValueError(
+                    f"{key} mismatch in worker checkpoint: "
+                    f"{state.get(key)} != {expected}"
+                )
+        packed = "pack_buffers" in state
+        if packed != (self._pack_sequences is not None):
+            raise ValueError("worker checkpoint mode does not match the dataset")
+        if packed:
+            for key in ("pack_sequences", "eos_id", "pad_id", "blocks_per_epoch"):
+                expected = getattr(self, f"_{key}")
+                if state.get(key) != expected:
+                    raise ValueError(
+                        f"{key} mismatch in worker checkpoint: "
+                        f"{state.get(key)} != {expected}"
+                    )
+            samples = state["samples_consumed_per_split"]
+            emitted = state["blocks_emitted_per_split"]
+            if len(samples) != self._num_splits or len(emitted) != self._num_splits:
+                raise ValueError(
+                    "packed worker checkpoint must contain one entry per split"
+                )
+            buffers = state["pack_buffers"]
+            for split, (count, blocks) in enumerate(zip(samples, emitted)):
+                incoming = (int(blocks), int(count))
+                current = (
+                    self._pack_blocks_emitted[split],
+                    self._pack_consumed[split],
+                )
+                if incoming > current:
+                    self._pack_blocks_emitted[split] = incoming[0]
+                    self._pack_consumed[split] = incoming[1]
+                    buffer = buffers.get(split, buffers.get(str(split)))
+                    if buffer is None:
+                        self._pack_buffers.pop(split, None)
+                    else:
+                        self._pack_buffers[split] = {
+                            "tokens": list(buffer["tokens"]),
+                            "starts": list(buffer["starts"]),
+                        }
+            self._consumer_checkpoint_requires_uniform |= require_uniform
+            return
+
+        samples = state["samples_consumed_per_split"]
+        positions = state.get("positions_consumed_per_split", samples)
+        for split, count in enumerate(samples):
+            current = self._resume_samples.get(split, self._resume_offset)
+            self._resume_samples[split] = max(current, int(count))
+        for split, position in enumerate(positions):
+            current = self._resume_positions.get(
+                split, self._resume_samples.get(split, self._resume_offset)
+            )
+            self._resume_positions[split] = max(current, int(position))
+        self._resume_offset = min(
+            self._resume_samples.get(split, self._resume_offset)
+            for split in range(self._num_splits)
+        )
+        self._consumer_checkpoint_requires_uniform |= require_uniform
+
    def load_state_dict(self, state: dict) -> None:
        """Resume from a previously snapshotted state.

@@ -1139,6 +1560,7 @@ class StreamingDataset(IterableDataset):
                f"shuffle_seed mismatch: checkpoint has {state['shuffle_seed']}, "
                f"current dataset has {self._shuffle_seed}"
            )
+        self._consumer_checkpoint_requires_uniform = False

        if "pack_buffers" in state or self._pack_sequences is not None:
            for key in (
@@ -1165,14 +1587,17 @@ class StreamingDataset(IterableDataset):
            return

        consumed = state["samples_consumed_per_split"]
-        # All entries are equal at step boundaries; use the first.
        if isinstance(consumed, list):
-            self._resume_offset = consumed[0] if consumed else 0
+            self._resume_offset = min(consumed) if consumed else 0
+            self._resume_samples = {
+                split: int(count) for split, count in enumerate(consumed)
+            }
        else:
            self._resume_offset = int(consumed)
+            self._resume_samples = {}
        # Older checkpoints predate positions_consumed_per_split; without
        # skipped rows positions equal sample counts, so falling back to
-        # _resume_offset (the .get default in __iter__) is exact.
+        # the per-split sample count (the .get default in __iter__) is exact.
        positions = state.get("positions_consumed_per_split")
        if positions is None:
            self._resume_positions = {}
@@ -1185,10 +1610,11 @@ class StreamingDataset(IterableDataset):
    def merge_state_dicts(states: list[dict]) -> dict:
        """Merge state dicts saved by different ranks into one exact state.

-        For row mode, the elementwise maximum of permutation positions recovers
-        splits advanced by different ranks after transform failures. For packed
-        mode, the state that emitted the most blocks for each logical split
-        supplies that split's permutation position and partial token buffer. Packed
+        In row mode, each rank records exact consumer-committed progress for
+        its own splits and lower bounds for the rest, so elementwise maxima
+        recover both sample counts and permutation positions. In packed mode,
+        the state that emitted the most blocks for each logical split supplies
+        that split's permutation position and partial token buffer. Packed
        states must cover every rank at the same global step.

        Raises ``ValueError`` if the states are empty, were not produced by
@@ -1299,17 +1725,13 @@ class StreamingDataset(IterableDataset):
            merged["pack_buffers"] = merged_buffers
            return merged

-        for state in states[1:]:
-            if (
-                state["samples_consumed_per_split"]
-                != first["samples_consumed_per_split"]
-            ):
-                raise ValueError(
-                    "samples_consumed_per_split mismatch across state dicts; "
-                    "state_dict() must be called at the same global step "
-                    "boundary on every rank"
-                )
        merged = dict(first)
+        merged["samples_consumed_per_split"] = [
+            max(per_split)
+            for per_split in zip(
+                *(state["samples_consumed_per_split"] for state in states)
+            )
+        ]
        all_positions = [
            state.get(
                "positions_consumed_per_split", state["samples_consumed_per_split"]
@@ -1320,3 +1742,113 @@ class StreamingDataset(IterableDataset):
            max(per_split) for per_split in zip(*all_positions)
        ]
        return merged
+
+
+class StreamingDataLoader(DataLoader):
+    """A PyTorch DataLoader with consumer-committed dataset checkpoints.
+
+    PyTorch workers prefetch batches ahead of the trainer, so worker-local
+    producer progress is not a safe checkpoint.  This loader carries a state
+    snapshot alongside every internal batch and applies it to the parent
+    [StreamingDataset][lancedb.streaming.StreamingDataset] only when that batch
+    is returned by ``next()``.
+    The trainer receives the same collated batch it would receive from a
+    standard ``torch.utils.data.DataLoader``.
+
+    With more than one worker, row-mode ``state_dict()`` is available only at
+    complete logical step boundaries, when every split assigned to the rank has
+    the same consumed-sample count. Packed checkpoints require equal emitted-block
+    counts across the rank's splits for any worker count. ``persistent_workers=True``
+    is not supported because prefetched worker copies cannot be restored from
+    parent-committed state. If batch collation raises, checkpointing remains
+    invalid for that dataset instance; restore the last valid checkpoint into a
+    fresh dataset before continuing.
+    Only one active iterator may own a dataset at a time, including when worker
+    processes are used. Exhausting or explicitly shutting down the iterator
+    releases that ownership. ``drop_last=True`` is not supported because worker
+    replicas discard incomplete tails independently, which cannot produce a
+    topology-independent checkpoint.
+
+    Parameters are the same as ``torch.utils.data.DataLoader`` except that
+    ``dataset`` must be a
+    [StreamingDataset][lancedb.streaming.StreamingDataset].
+    Subclasses that override ``StreamingDataset.__iter__`` are not supported
+    because the custom iterator cannot provide the exact per-yield checkpoint
+    snapshots required by this loader.
+
+    Examples
+    --------
+    >>> # dataset = StreamingDataset(table, num_splits=2)
+    >>> # loader = StreamingDataLoader(dataset, batch_size=8, num_workers=2)
+    >>> # batch = next(iter(loader))
+    >>> # checkpoint = dataset.state_dict()
+    """
+
+    def __init__(self, dataset: StreamingDataset, *args, **kwargs):
+        if not isinstance(dataset, StreamingDataset):
+            raise TypeError("StreamingDataLoader requires a StreamingDataset")
+        if type(dataset).__iter__ is not StreamingDataset.__iter__:
+            raise TypeError(
+                "StreamingDataLoader does not support StreamingDataset subclasses "
+                "that override __iter__ because they cannot provide exact "
+                "per-yield checkpoint state"
+            )
+        if kwargs.get("in_order", True) is False:
+            raise ValueError(
+                "StreamingDataLoader requires in_order=True for deterministic "
+                "consumer checkpoints"
+            )
+        if kwargs.get("persistent_workers", False):
+            raise ValueError(
+                "StreamingDataLoader does not support persistent_workers=True "
+                "because worker prefetch state cannot be reset from a checkpoint"
+            )
+        self._streaming_dataset = dataset
+        super().__init__(_StreamingDatasetAdapter(dataset), *args, **kwargs)
+        if self.drop_last:
+            raise ValueError(
+                "StreamingDataLoader does not support drop_last=True because "
+                "discarded worker tails cannot be checkpointed "
+                "topology-independently"
+            )
+        self.collate_fn = _CheckpointCollate(self.collate_fn)
+
+    def __iter__(self):
+        dataset = self._streaming_dataset
+        previous_lease = dataset._consumer_iterator_lease
+        owner_token = None
+        try:
+            owner_token = dataset._acquire_consumer_iterator()
+            state = dataset._checkpoint_snapshot()
+            packed = dataset._pack_sequences is not None
+            if packed:
+                blocks = state["blocks_emitted_per_split"]
+                rank_blocks = [blocks[split] for split in dataset._rank_splits]
+                if len(set(rank_blocks)) > 1:
+                    raise RuntimeError(
+                        "StreamingDataLoader cannot start from a partial packed "
+                        "logical step; resume from a checkpoint whose splits "
+                        "assigned to this rank have equal emitted-block counts"
+                    )
+            elif self.num_workers > 1:
+                samples = state["samples_consumed_per_split"]
+                rank_samples = [samples[split] for split in dataset._rank_splits]
+                if len(set(rank_samples)) > 1:
+                    raise RuntimeError(
+                        "StreamingDataLoader cannot start multiple workers from a "
+                        "partial logical step; resume from a checkpoint whose "
+                        "splits assigned to this rank have equal consumed-sample "
+                        "counts"
+                    )
+            return _ConsumerCommitIterator(
+                super().__iter__(),
+                dataset,
+                owner_token=owner_token,
+                require_uniform=self.num_workers > 1 or packed,
+            )
+        except BaseException:
+            if owner_token is not None:
+                dataset._release_consumer_iterator(owner_token)
+            else:
+                dataset._release_consumer_iterator_after_failed_acquire(previous_lease)
+            raise
@@ -1269,6 +1269,7 @@ class Table(ABC):
        fill_value: float = 0.0,
        progress: Optional[Union[bool, Callable, Any]] = None,
        write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult:
        """Add more data to the [Table][lancedb.table.Table].

@@ -1320,6 +1321,10 @@ class Table(ABC):
            data in flight. Defaults to an estimate based on the data size,
            capped at the number of CPU cores. Lower this if bulk ingestion is
            using too much memory.
+        allow_external_blob_outside_bases: bool, default False
+            Store blob URIs that sit outside registered blob bases. The row
+            keeps a reference, so the object has to stay readable. Local
+            tables only.

        Returns
        -------
@@ -1972,7 +1977,7 @@ class Table(ABC):
            A mapping with one ``FunctionApplication`` value keeps its scalar
            or named-struct result in the named table column. A bare
            named-struct application expands its ordered result fields as one
-            atomic sibling group; aliases come from ``rename(columns=...)``.
+            atomic binding; aliases come from ``rename(columns=...)``.
            Function columns are supported only on LanceDB Cloud and
            Enterprise.
        computed: Dict[str, str], optional
@@ -3409,6 +3414,7 @@ class LanceTable(Table):
        fill_value: float = 0.0,
        progress: Optional[Union[bool, Callable, Any]] = None,
        write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult:
        """Add data to the table.
        If vector columns are missing and the table
@@ -3436,6 +3442,9 @@ class LanceTable(Table):
            data in flight. Defaults to an estimate based on the data size,
            capped at the number of CPU cores. Lower this if bulk ingestion is
            using too much memory.
+        allow_external_blob_outside_bases: bool, default False
+            Allow blob URIs outside registered bases. See :meth:`Table.add`.
+            Local tables only.

        Returns
        -------
@@ -3452,6 +3461,7 @@ class LanceTable(Table):
                    fill_value=fill_value,
                    progress=progress,
                    write_parallelism=write_parallelism,
+                    allow_external_blob_outside_bases=allow_external_blob_outside_bases,
                )
            )
        finally:
@@ -5365,6 +5375,7 @@ class AsyncTable:
        fill_value: Optional[float] = None,
        progress: Optional[Union[bool, Callable, Any]] = None,
        write_parallelism: Optional[int] = None,
+        allow_external_blob_outside_bases: bool = False,
    ) -> AddResult:
        """Add more data to the [AsyncTable][lancedb.table.AsyncTable].

@@ -5395,6 +5406,9 @@ class AsyncTable:
            data in flight. Defaults to an estimate based on the data size,
            capped at the number of CPU cores. Lower this if bulk ingestion is
            using too much memory.
+        allow_external_blob_outside_bases: bool, default False
+            Allow blob URIs outside registered bases. See :meth:`Table.add`.
+            Local tables only.

        """
        schema = await self.schema()
@@ -5431,6 +5445,7 @@ class AsyncTable:
                mode or "append",
                progress=progress,
                write_parallelism=write_parallelism,
+                allow_external_blob_outside_bases=allow_external_blob_outside_bases,
            )
        except RuntimeError as e:
            if "Cast error" in str(e):
@@ -6038,7 +6053,7 @@ class AsyncTable:
            A mapping with one ``FunctionApplication`` value keeps its scalar
            or named-struct result in the named table column. A bare
            named-struct application expands its ordered result fields as one
-            atomic sibling group; aliases come from ``rename(columns=...)``.
+            atomic binding; aliases come from ``rename(columns=...)``.
            Function columns are supported only on LanceDB Cloud and
            Enterprise.
        computed: Dict[str, str], optional
@@ -6075,7 +6090,7 @@ class AsyncTable:
                isinstance(value, FunctionApplication) for value in transforms.values()
            ):
                raise ValueError(
-                    "one add_columns call declares exactly one Function sibling group"
+                    "one add_columns call declares exactly one Function binding"
                )
            function_output_name, function_application = next(iter(transforms.items()))

@@ -617,3 +617,71 @@ def test_fetch_blobs_nested_path_survives_sort_after_query():
 def _identifiable_payload(size: int) -> bytes:
    block = 256
    return b"".join(bytes([i % 256]) * block for i in range(size // block))
+
+
+def _external_uri_blob_array(uris):
+    blob_type = lancedb.blob("image").type
+    storage_type = blob_type.storage_type
+    child_names = [field.name for field in storage_type]
+    assert "uri" in child_names, "blob layout no longer has a uri child"
+    children = [
+        pa.array(uris if field.name == "uri" else [None] * len(uris), type=field.type)
+        for field in storage_type
+    ]
+    storage = pa.StructArray.from_arrays(children, fields=list(storage_type))
+    return pa.ExtensionArray.from_storage(blob_type, storage)
+
+
+def _external_uri_table_and_rows(name, uris):
+    db = lancedb.connect("memory:///")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table(name, schema=schema)
+    rows = pa.Table.from_arrays(
+        [
+            pa.array(range(len(uris)), type=pa.int64()),
+            _external_uri_blob_array(uris),
+        ],
+        schema=schema,
+    )
+    return table, rows
+
+
+def test_add_external_uri_struct_round_trips_with_flag(tmp_path):
+    payload = b"external-uri-bytes"
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(payload)
+
+    table, rows = _external_uri_table_and_rows("external_struct", [blob_path.as_uri()])
+    table.add(rows, allow_external_blob_outside_bases=True)
+
+    hits = table.search().to_arrow()
+    blobs = table.fetch_blobs("image", hits)
+    assert blobs[0].as_py() == payload
+
+
+def test_add_external_uri_without_flag_raises(tmp_path):
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(b"unreachable")
+
+    table, rows = _external_uri_table_and_rows("external_no_flag", [blob_path.as_uri()])
+    with pytest.raises(ValueError, match="allow_external_blob_outside_bases"):
+        table.add(rows)
+    assert table.count_rows() == 0
+
+
+def test_add_external_uri_string_round_trips_with_flag(tmp_path):
+    payload = b"external-uri-bytes"
+    blob_path = tmp_path / "payload.bin"
+    blob_path.write_bytes(payload)
+
+    db = lancedb.connect("memory:///")
+    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
+    table = db.create_table("external_string", schema=schema)
+    table.add(
+        [{"id": 1, "image": blob_path.as_uri()}],
+        allow_external_blob_outside_bases=True,
+    )
+
+    hits = table.search().to_arrow()
+    blobs = table.fetch_blobs("image", hits)
+    assert blobs[0].as_py() == payload
@@ -32,6 +32,7 @@ Parameters used throughout:

 import dataclasses
 import logging
+import threading
 from unittest.mock import patch

 import lancedb
@@ -46,6 +47,7 @@ from utils import (
 torch = pytest.importorskip("torch")
 streaming = pytest.importorskip("lancedb.streaming")
 StreamingDataset = streaming.StreamingDataset
+StreamingDataLoader = streaming.StreamingDataLoader

 # ---------------------------------------------------------------------------
 # Dataset parameters
@@ -92,6 +94,27 @@ class FakeWorkerInfo:
    num_workers: int


+def _collate_with_first_batch_error(samples):
+    ids = [sample["id"] for sample in samples]
+    if ids == [0, 1]:
+        raise ValueError("first batch fails")
+    return ids
+
+
+def _collate_with_first_batch_stop(samples):
+    ids = [sample["id"] for sample in samples]
+    if ids == [0, 1]:
+        raise StopIteration("first batch stopped")
+    return ids
+
+
+def _collate_with_first_batch_interrupt(samples):
+    ids = [sample["id"] for sample in samples]
+    if ids == [0, 1]:
+        raise KeyboardInterrupt("first batch interrupted")
+    return ids
+
+
 # ---------------------------------------------------------------------------
 # Fixtures
 # ---------------------------------------------------------------------------
@@ -1008,6 +1031,565 @@ def test_multi_worker_elastic_det_across_worker_counts(lance_table):
 # ── Resumability with num_workers ─────────────────────────────────────────────


+def test_streaming_dataloader_commits_only_consumed_worker_batches(tmp_path):
+    """Prefetched worker state is committed only as the trainer receives it."""
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "worker_commit", pa.table({"id": [1, 2, 3, 4, 10, 20, 30, 40]})
+    )
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        prefetch_factor=4,
+    )
+    iterator = iter(loader)
+    try:
+        first = next(iterator)["id"].tolist()
+
+        assert first == [1, 2]
+        assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [2, 0]
+        with pytest.raises(RuntimeError, match="complete logical step boundary"):
+            dataset.state_dict()
+
+        second = next(iterator)["id"].tolist()
+        assert second == [10, 20]
+        checkpoint = dataset.state_dict()
+        assert checkpoint["samples_consumed_per_split"] == [2, 2]
+        uninterrupted = [batch["id"].tolist() for batch in iterator]
+    finally:
+        iterator._shutdown_workers()
+
+    resumed = StreamingDataset(table, num_splits=2, shuffle=False)
+    resumed.load_state_dict(checkpoint)
+    resumed_loader = StreamingDataLoader(
+        resumed,
+        batch_size=2,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        prefetch_factor=4,
+    )
+    resumed_iterator = iter(resumed_loader)
+    try:
+        remaining = [batch["id"].tolist() for batch in resumed_iterator]
+    finally:
+        resumed_iterator._shutdown_workers()
+    assert remaining == uninterrupted == [[3, 4], [30, 40]]
+
+
+def test_distributed_checkpoint_uses_rank_local_worker_boundary(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("rank_boundary", pa.table({"id": list(range(8))}))
+    dataset = StreamingDataset(
+        table,
+        num_splits=4,
+        shuffle=False,
+        rank=0,
+        world_size=2,
+    )
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=1,
+        num_workers=2,
+        multiprocessing_context="spawn",
+    )
+    iterator = iter(loader)
+    try:
+        assert next(iterator)["id"].tolist() == [0]
+        assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [
+            1,
+            0,
+            0,
+            0,
+        ]
+        with pytest.raises(RuntimeError, match="complete logical step boundary"):
+            dataset.state_dict()
+
+        assert next(iterator)["id"].tolist() == [2]
+        checkpoint = dataset.state_dict()
+        remaining = [batch["id"].tolist() for batch in iterator]
+    finally:
+        iterator._shutdown_workers()
+
+    assert checkpoint["samples_consumed_per_split"] == [1, 1, 0, 0]
+    assert remaining == [[1], [3]]
+
+
+def test_standard_dataloader_rejects_stale_parent_checkpoint(tmp_path):
+    """A standard DataLoader must not expose prefetched producer progress."""
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("untracked_workers", pa.table({"id": [1, 2, 10, 20]}))
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+    # Merely constructing the checkpoint-aware loader must not authorize a
+    # later plain DataLoader's worker progress.
+    StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+    loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=2,
+        multiprocessing_context="spawn",
+    )
+    iterator = iter(loader)
+    try:
+        assert next(iterator)["id"].tolist() == [1, 2]
+        with pytest.raises(RuntimeError, match="Use StreamingDataLoader"):
+            dataset.state_dict()
+        list(iterator)
+    finally:
+        iterator._shutdown_workers()
+
+
+def test_streaming_dataloader_rejects_persistent_workers(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("persistent_workers", pa.table({"id": [1, 2]}))
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+
+    with pytest.raises(ValueError, match="persistent_workers=True"):
+        StreamingDataLoader(
+            dataset,
+            batch_size=1,
+            num_workers=2,
+            persistent_workers=True,
+        )
+
+
+def test_collate_failure_invalidates_consumer_checkpoint(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "collate_failure", pa.table({"id": [0, 1, 2, 3, 100, 101, 102, 103]})
+    )
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        collate_fn=_collate_with_first_batch_error,
+        prefetch_factor=2,
+    )
+    iterator = iter(loader)
+    try:
+        with pytest.raises(ValueError, match="first batch fails"):
+            next(iterator)
+        assert next(iterator) == [100, 101]
+        assert next(iterator) == [2, 3]
+        with pytest.raises(RuntimeError, match="failed before it was returned"):
+            dataset.state_dict()
+        list(iterator)
+    finally:
+        iterator._shutdown_workers()
+
+
+def test_collate_stop_iteration_invalidates_consumer_checkpoint(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("collate_stop", pa.table({"id": list(range(6))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=0,
+        collate_fn=_collate_with_first_batch_stop,
+    )
+    iterator = iter(loader)
+
+    with pytest.raises(RuntimeError, match="collate_fn raised StopIteration"):
+        next(iterator)
+    assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [2]
+    with pytest.raises(RuntimeError, match="failed before it was returned"):
+        dataset.state_dict()
+    assert list(iterator) == [[2, 3], [4, 5]]
+
+
+def test_batch_base_exception_invalidates_consumer_checkpoint(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("collate_interrupt", pa.table({"id": list(range(6))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=0,
+        collate_fn=_collate_with_first_batch_interrupt,
+    )
+    iterator = iter(loader)
+
+    with pytest.raises(KeyboardInterrupt, match="first batch interrupted"):
+        next(iterator)
+    assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [2]
+    with pytest.raises(RuntimeError, match="failed before it was returned"):
+        dataset.state_dict()
+    assert list(iterator) == [[2, 3], [4, 5]]
+
+
+def test_parent_commit_base_exception_invalidates_consumer_checkpoint(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("commit_interrupt", pa.table({"id": list(range(4))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+    iterator = iter(loader)
+    real_commit = dataset._commit_worker_state
+
+    def interrupt_after_commit(state, *, require_uniform):
+        real_commit(state, require_uniform=require_uniform)
+        raise KeyboardInterrupt("after parent commit")
+
+    with patch.object(
+        dataset, "_commit_worker_state", side_effect=interrupt_after_commit
+    ):
+        with pytest.raises(KeyboardInterrupt, match="after parent commit"):
+            next(iterator)
+
+    assert dataset._checkpoint_snapshot()["samples_consumed_per_split"] == [2]
+    with pytest.raises(RuntimeError, match="failed before it was returned"):
+        dataset.state_dict()
+
+
+def test_direct_iteration_surfaces_prefetch_failure_before_committing_row(
+    tmp_path, monkeypatch
+):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("prefetch_failure", pa.table({"id": list(range(4))}))
+    release = threading.Event()
+    failed = threading.Event()
+    real_getitems = streaming.Permutation.__getitems__
+
+    def controlled_getitems(permutation, indices):
+        if indices and indices[0] >= 2:
+            assert release.wait(timeout=5)
+            failed.set()
+            raise RuntimeError("later prefetched I/O failed")
+        return real_getitems(permutation, indices)
+
+    class SignalDict(dict):
+        def __setitem__(self, key, value):
+            super().__setitem__(key, value)
+            release.set()
+            assert failed.wait(timeout=5)
+
+    monkeypatch.setattr(streaming.Permutation, "__getitems__", controlled_getitems)
+    dataset = StreamingDataset(
+        table,
+        num_splits=1,
+        shuffle=False,
+        read_batch_size=2,
+        io_queue_depth=2,
+    )
+    dataset._resume_positions = SignalDict()
+    iterator = iter(dataset)
+
+    assert next(iterator)["id"] == 0
+    with pytest.raises(RuntimeError, match="later prefetched I/O failed"):
+        next(iterator)
+
+    checkpoint = dataset.state_dict()
+    assert checkpoint["samples_consumed_per_split"] == [1]
+    assert checkpoint["positions_consumed_per_split"] == [1]
+
+
+@pytest.mark.parametrize("workers", [0, 1, 2])
+def test_streaming_dataloader_rejects_drop_last(tmp_path, workers):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("drop_last", pa.table({"id": [0, 1, 2]}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    worker_options = {"multiprocessing_context": "spawn"} if workers else {}
+
+    with pytest.raises(ValueError, match="drop_last=True"):
+        StreamingDataLoader(
+            dataset,
+            batch_size=2,
+            num_workers=workers,
+            drop_last=True,
+            **worker_options,
+        )
+
+
+def test_streaming_dataloader_owns_one_iterator_until_teardown(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("iterator_owner", pa.table({"id": list(range(4))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=2,
+        num_workers=1,
+        multiprocessing_context="spawn",
+    )
+
+    first = iter(loader)
+    try:
+        assert next(first)["id"].tolist() == [0, 1]
+        with pytest.raises(RuntimeError, match="concurrent iteration"):
+            iter(loader)
+    finally:
+        first._shutdown_workers()
+
+    second = iter(loader)
+    try:
+        assert [batch["id"].tolist() for batch in second] == [[2, 3]]
+    except BaseException:
+        second._shutdown_workers()
+        raise
+
+    # Natural exhaustion releases ownership too.
+    third = iter(loader)
+    try:
+        assert list(third) == []
+    finally:
+        third._shutdown_workers()
+
+
+def test_zero_worker_shutdown_closes_inner_iterator_before_release(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("zero_worker_shutdown", pa.table({"id": list(range(6))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+
+    first = iter(loader)
+    assert next(first)["id"].tolist() == [0, 1]
+    first._shutdown_workers()
+
+    assert dataset._consumer_iterator_active is False
+    assert dataset._raw_batches_ref is None
+    second = iter(loader)
+    try:
+        with pytest.raises(StopIteration):
+            next(first)
+        assert next(second)["id"].tolist() == [2, 3]
+    finally:
+        second._shutdown_workers()
+
+
+def test_direct_and_loader_admission_share_one_atomic_lease(tmp_path, monkeypatch):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("direct_loader_lease", pa.table({"id": list(range(4))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+    entered = threading.Event()
+    release = threading.Event()
+    direct_result = []
+    direct_error = []
+    contender = []
+    real_resolve = dataset._resolve_my_splits
+
+    def controlled_resolve():
+        if threading.current_thread().name == "direct-start":
+            entered.set()
+            assert release.wait(timeout=5)
+        return real_resolve()
+
+    def advance_direct(iterator):
+        try:
+            direct_result.append(next(iterator)["id"])
+        except BaseException as exc:
+            direct_error.append(exc)
+
+    monkeypatch.setattr(dataset, "_resolve_my_splits", controlled_resolve)
+    direct = iter(dataset)
+    thread = threading.Thread(
+        target=advance_direct, args=(direct,), name="direct-start"
+    )
+    thread.start()
+    assert entered.wait(timeout=5)
+    try:
+        with pytest.raises(RuntimeError, match="concurrent iteration"):
+            contender.append(iter(loader))
+    finally:
+        release.set()
+        thread.join(timeout=5)
+        if contender:
+            contender[0]._shutdown_workers()
+        direct.close()
+
+    assert not thread.is_alive()
+    assert direct_error == []
+    assert direct_result == [0]
+
+
+def test_loader_acquires_before_snapshot_and_cleans_interrupted_acquire(
+    tmp_path, monkeypatch
+):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("lease_snapshot", pa.table({"id": list(range(4))}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=2, num_workers=0)
+    first = iter(loader)
+    assert next(first)["id"].tolist() == [0, 1]
+
+    entered = threading.Event()
+    release = threading.Event()
+    pending = []
+    pending_errors = []
+    observed_snapshots = []
+    real_acquire = dataset._acquire_consumer_iterator
+    real_snapshot = dataset._checkpoint_snapshot
+
+    def controlled_acquire():
+        if threading.current_thread().name == "stale-start":
+            entered.set()
+            assert release.wait(timeout=5)
+        return real_acquire()
+
+    def recording_snapshot():
+        state = real_snapshot()
+        if threading.current_thread().name == "stale-start":
+            observed_snapshots.append(state["samples_consumed_per_split"])
+        return state
+
+    def create_pending_iterator():
+        try:
+            pending.append(iter(loader))
+        except BaseException as exc:
+            pending_errors.append(exc)
+
+    monkeypatch.setattr(dataset, "_acquire_consumer_iterator", controlled_acquire)
+    monkeypatch.setattr(dataset, "_checkpoint_snapshot", recording_snapshot)
+    thread = threading.Thread(target=create_pending_iterator, name="stale-start")
+    thread.start()
+    assert entered.wait(timeout=5)
+    assert next(first)["id"].tolist() == [2, 3]
+    with pytest.raises(StopIteration):
+        next(first)
+    release.set()
+    thread.join(timeout=5)
+
+    assert not thread.is_alive()
+    assert pending_errors == []
+    assert observed_snapshots == [[4]]
+    assert len(pending) == 1
+    assert list(pending[0]) == []
+    assert dataset.state_dict()["samples_consumed_per_split"] == [4]
+
+    def interrupted_acquire():
+        real_acquire()
+        raise KeyboardInterrupt("after acquire")
+
+    monkeypatch.setattr(dataset, "_acquire_consumer_iterator", interrupted_acquire)
+    with pytest.raises(KeyboardInterrupt, match="after acquire"):
+        iter(loader)
+    assert dataset._consumer_iterator_active is False
+
+
+def test_consumer_iterator_lease_publication_is_atomic(tmp_path, monkeypatch):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("atomic_lease", pa.table({"id": [0, 1]}))
+    dataset = StreamingDataset(table, num_splits=1, shuffle=False)
+    loader = StreamingDataLoader(dataset, batch_size=1, num_workers=0)
+    real_get_ident = streaming.threading.get_ident
+    calls = 0
+
+    def interrupt_during_publication():
+        nonlocal calls
+        calls += 1
+        if calls == 1:
+            raise KeyboardInterrupt("during lease mutation")
+        return real_get_ident()
+
+    monkeypatch.setattr(streaming.threading, "get_ident", interrupt_during_publication)
+    with pytest.raises(KeyboardInterrupt, match="during lease mutation"):
+        iter(loader)
+    monkeypatch.setattr(streaming.threading, "get_ident", real_get_ident)
+
+    assert dataset._consumer_iterator_active is False
+    iterator = iter(loader)
+    try:
+        assert next(iterator)["id"].tolist() == [0]
+    finally:
+        iterator._shutdown_workers()
+
+
+def test_streaming_dataloader_rejects_dataset_iter_override(tmp_path):
+    class CustomizedDataset(StreamingDataset):
+        def __iter__(self):
+            return iter([1000, 1001])
+
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("custom_iteration", pa.table({"id": [0, 1, 2]}))
+    dataset = CustomizedDataset(table, num_splits=1, shuffle=False)
+
+    assert list(dataset) == [1000, 1001]
+    with pytest.raises(TypeError, match="override __iter__"):
+        StreamingDataLoader(
+            dataset,
+            batch_size=2,
+            num_workers=0,
+            collate_fn=list,
+        )
+
+
+def test_interleaved_adapters_do_not_authorize_plain_iteration(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table_a = db.create_table("adapter_a", pa.table({"id": [0, 1]}))
+    table_b = db.create_table("adapter_b", pa.table({"id": [10, 11]}))
+    dataset_a = StreamingDataset(table_a, num_splits=1, shuffle=False)
+    dataset_b = StreamingDataset(table_b, num_splits=1, shuffle=False)
+    initial_state = dataset_a.state_dict()
+
+    owner_a = dataset_a._acquire_consumer_iterator()
+    owner_b = dataset_b._acquire_consumer_iterator()
+    try:
+        iterator_a = iter(streaming._StreamingDatasetAdapter(dataset_a))
+        iterator_b = iter(streaming._StreamingDatasetAdapter(dataset_b))
+        assert next(iterator_a).data["id"] == 0
+        assert next(iterator_b).data["id"] == 10
+        assert [sample.data["id"] for sample in iterator_a] == [1]
+        assert [sample.data["id"] for sample in iterator_b] == [11]
+    finally:
+        dataset_a._release_consumer_iterator(owner_a)
+        dataset_b._release_consumer_iterator(owner_b)
+
+    dataset_a.load_state_dict(initial_state)
+    with patch(
+        "lancedb.streaming.get_worker_info",
+        return_value=FakeWorkerInfo(id=0, num_workers=1),
+    ):
+        plain_iterator = iter(dataset_a)
+        assert next(plain_iterator)["id"] == 0
+        plain_iterator.close()
+
+    assert dataset_a._untracked_worker_iteration[0] == 1
+    with pytest.raises(RuntimeError, match="Use StreamingDataLoader"):
+        dataset_a.state_dict()
+
+
+def test_resume_from_partial_split_cycle_preserves_remaining_order(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table("partial_cycle", pa.table({"id": [1, 2, 10, 20]}))
+    dataset = StreamingDataset(table, num_splits=2, shuffle=False)
+    iterator = iter(dataset)
+
+    assert next(iterator)["id"] == 1
+    checkpoint = dataset.state_dict()
+    iterator.close()
+    assert checkpoint["samples_consumed_per_split"] == [1, 0]
+
+    resumed = StreamingDataset(table, num_splits=2, shuffle=False)
+    resumed.load_state_dict(checkpoint)
+    assert [row["id"] for row in resumed] == [10, 2, 20]
+
+
+def test_partial_cycle_resume_preserves_skip_truncation(tmp_path):
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "partial_skip", pa.table({"id": [0, 1, 2, 3, 100, 101, 102, 103]})
+    )
+    kwargs = dict(
+        num_splits=2,
+        shuffle=False,
+        transform=_failing_transform({1, 2, 3}),
+        on_transform_error="skip",
+    )
+    dataset = StreamingDataset(table, **kwargs)
+    iterator = iter(dataset)
+
+    assert next(iterator)["id"] == 0
+    checkpoint = dataset.state_dict()
+    uninterrupted = [row["id"] for row in iterator]
+
+    resumed = StreamingDataset(table, **kwargs)
+    resumed.load_state_dict(checkpoint)
+    assert [row["id"] for row in resumed] == uninterrupted == [100]
+
+
 def test_multi_worker_resumability_same_topology(lance_table):
    """Checkpoint with num_workers=2, resume with num_workers=2: exact continuation."""
    world_size = 1
@@ -2018,6 +2600,23 @@ def test_merge_state_dicts_validates_consistency(lance_table):
        StreamingDataset.merge_state_dicts([])


+def test_merge_state_dicts_combines_nonuniform_consumer_progress(lance_table):
+    dataset = StreamingDataset(
+        lance_table, num_splits=2, shuffle=False, shuffle_seed=SHUFFLE_SEED
+    )
+    rank0 = dataset.state_dict()
+    rank0["samples_consumed_per_split"] = [2, 0]
+    rank0["positions_consumed_per_split"] = [2, 0]
+    rank1 = dataset.state_dict()
+    rank1["samples_consumed_per_split"] = [0, 2]
+    rank1["positions_consumed_per_split"] = [0, 2]
+
+    merged = StreamingDataset.merge_state_dicts([rank0, rank1])
+
+    assert merged["samples_consumed_per_split"] == [2, 2]
+    assert merged["positions_consumed_per_split"] == [2, 2]
+
+
 def test_load_state_dict_without_positions_key(lance_table):
    """Checkpoints from before positions_consumed_per_split existed still
    resume exactly (positions equal sample counts when nothing is skipped)."""
@@ -2254,6 +2853,65 @@ def test_pack_sequences_checkpoint_resumes_on_new_topology(tmp_path):
    ]


+def test_packed_checkpoint_requires_complete_split_cycle(tmp_path):
+    table = _create_token_table(tmp_path, [[1], [2], [10], [20]])
+    dataset = _packed_dataset(table, pack_sequences=3, blocks_per_epoch=4, num_splits=2)
+    iterator = iter(dataset)
+
+    next(iterator)
+    with pytest.raises(RuntimeError, match="complete logical step boundary"):
+        dataset.state_dict()
+
+    next(iterator)
+    assert dataset.state_dict()["blocks_emitted_per_split"] == [1, 1]
+    iterator.close()
+
+
+def test_streaming_dataloader_commits_consumed_packed_batches(tmp_path):
+    table = _create_token_table(
+        tmp_path,
+        [[1], [2], [3], [4], [10], [20], [30], [40]],
+    )
+    kwargs = dict(pack_sequences=4, blocks_per_epoch=4, num_splits=2)
+    dataset = _packed_dataset(table, **kwargs)
+    loader = StreamingDataLoader(
+        dataset,
+        batch_size=1,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        prefetch_factor=2,
+    )
+    iterator = iter(loader)
+    try:
+        next(iterator)
+        with pytest.raises(RuntimeError, match="complete logical step boundary"):
+            dataset.state_dict()
+
+        next(iterator)
+        checkpoint = dataset.state_dict()
+        uninterrupted = [batch["input_ids"].tolist() for batch in iterator]
+    finally:
+        iterator._shutdown_workers()
+
+    resumed = _packed_dataset(table, **kwargs)
+    resumed.load_state_dict(checkpoint)
+    resumed_loader = StreamingDataLoader(
+        resumed,
+        batch_size=1,
+        num_workers=2,
+        multiprocessing_context="spawn",
+        prefetch_factor=2,
+    )
+    resumed_iterator = iter(resumed_loader)
+    try:
+        remaining = [batch["input_ids"].tolist() for batch in resumed_iterator]
+    finally:
+        resumed_iterator._shutdown_workers()
+
+    assert checkpoint["blocks_emitted_per_split"] == [1, 1]
+    assert remaining == uninterrupted
+
+
 def test_pack_sequences_validates_configuration_and_tokens(tmp_path):
    table = _create_token_table(tmp_path, [[1, 2]])

@@ -37,21 +37,6 @@ def job_result(name: str) -> dict:
    return json.loads(fixture(name))["result"]


-def assert_no_secret_values(value):
-    if isinstance(value, dict):
-        for key, child in value.items():
-            assert key not in {
-                "secret_value",
-                "secret_values",
-                "resolved_secret",
-                "resolved_secrets",
-            }
-            assert_no_secret_values(child)
-    elif isinstance(value, list):
-        for child in value:
-            assert_no_secret_values(child)
-
-
 def test_public_function_values_are_in_api_reference():
    docs = Path(__file__).parents[3] / "docs" / "src" / "python" / "python.md"
    rendered = docs.read_text()
@@ -109,7 +94,6 @@ def test_function_version_identity_is_immutable_and_exact():
    version = FunctionVersion.from_json(json.dumps(value))
    assert version.name == "embed"
    assert version.version == "fv_01K3EXACT"
-    assert version.required_secrets == ("HF_TOKEN",)

    with pytest.raises((TypeError, ValueError)):
        version.version = "fv_changed"
@@ -121,7 +105,7 @@ def test_function_version_identity_is_immutable_and_exact():
    assert FunctionVersion(**changed) != version


-def test_function_version_binds_named_columns_as_one_immutable_group():
+def test_function_version_binds_named_columns_as_one_immutable_application():
    version = FunctionVersion.from_json(
        json.dumps(job_result("remote_function_job.json"))
    )
@@ -131,13 +115,10 @@ def test_function_version_binds_named_columns_as_one_immutable_group():
    assert application.function.name == version.name
    assert application.function.version == version.version
    assert application.output is version.signature.output
-    assert application.group_id.startswith("fg_")
    assert [
        (value.parameter, value.kind, value.value["path"])
        for value in application.inputs
    ] == [("text", "column", "documents.body")]
-    with pytest.raises((TypeError, ValueError)):
-        application.group_id = "fg_changed"


 def test_function_version_binding_validates_names_and_direct_columns():
@@ -156,7 +137,7 @@ def test_function_version_binding_validates_names_and_direct_columns():
 def test_function_version_keeps_named_struct_outputs_in_one_application():
    value = job_result("remote_function_job.json")
    value["name"] = "text_features"
-    value["version"] = "fv_grouped"
+    value["version"] = "fv_multi_output"
    value["signature"] = {
        "inputs": [
            {"name": "title", "arrow_type": "utf8", "nullable": True},
@@ -221,7 +202,6 @@ def test_function_application_uses_rename_columns_only():
    assert application.columns["normalized_text"] == "search_text"
    assert renamed.columns["normalized_text"] == "body_normalized"
    assert renamed.function == application.function
-    assert renamed.group_id == application.group_id
    assert not hasattr(application, "rename_outputs")
    with pytest.raises(TypeError, match="immutable"):
        renamed.columns["normalized_text"] = "changed"
@@ -242,7 +222,6 @@ def test_function_application_uses_rename_columns_only():

 def test_binding_and_refresh_result_keep_stable_remote_fields():
    binding = FunctionBinding.from_json(fixture("remote_function_binding.json"))
-    assert binding.revision == 3
    assert binding.function.version == "fv_01K3TEXT"
    assert [output.output_ordinal for output in binding.outputs] == [0, 1]
    assert binding.input_schema is not None
@@ -297,15 +276,6 @@ def test_refresh_result_rejects_non_u64_values(field):
        RefreshColumnResult.from_json(json.dumps(value))


-def test_canonical_client_values_contain_secret_names_only():
-    version = FunctionVersion.from_json(
-        json.dumps(job_result("remote_function_job.json"))
-    )
-    canonical = json.loads(version.to_canonical_json())
-    assert canonical["required_secrets"] == ["HF_TOKEN"]
-    assert_no_secret_values(canonical)
-
-
 class _FunctionDeclarationInner:
    def __init__(self):
        self.calls = []
@@ -322,7 +292,7 @@ def known_application() -> FunctionApplication:


@pytest.mark.asyncio
-async def test_add_columns_routes_struct_as_one_and_grouped_expansion_atomically():
+async def test_add_columns_routes_struct_as_one_and_multi_output_binding_atomically():
    inner = _FunctionDeclarationInner()
    table = AsyncTable(inner)
    application = known_application()
@@ -343,12 +313,12 @@ async def test_add_columns_routes_struct_as_one_and_grouped_expansion_atomically


@pytest.mark.asyncio
-async def test_add_columns_rejects_mixed_groups_and_unknown_newer_application():
+async def test_add_columns_rejects_multiple_bindings_and_unknown_newer_application():
    inner = _FunctionDeclarationInner()
    table = AsyncTable(inner)
    application = known_application()

-    with pytest.raises(ValueError, match="exactly one Function sibling group"):
+    with pytest.raises(ValueError, match="exactly one Function binding"):
        await table.add_columns({"a": application, "b": application})

    future = json.loads(fixture("remote_function_application.json"))
@@ -376,7 +346,6 @@ def test_rename_requires_named_struct_and_keeps_partial_mapping_immutable():
                    "arrow_type": "list<float32>",
                    "nullable": False,
                },
-                "group_id": "fg_scalar",
            }
        )
    )
@@ -3,7 +3,12 @@

 from __future__ import annotations

+import base64
 import contextlib
+import functools
+import importlib.util
+import types
+from datetime import date
 import http.server
 import json
 from pathlib import Path
@@ -16,6 +21,9 @@ import pytest
 import lancedb
 from lancedb.functions import UdfDefinition, udf

+THRESHOLD = 20
+_CACHE = None
+

 FIXTURES = (
    Path(__file__).parents[3]
@@ -31,28 +39,12 @@ FIXTURES = (
@udf(
    pip=["numpy>=2"],
    env={"MODE": "test"},
-    secrets=["API_TOKEN"],
    python_version="3.12",
 )
 def normalize_score(value: float) -> float:
    return value / 100.0


-def _assert_no_secret_values(value):
-    if isinstance(value, dict):
-        for key, child in value.items():
-            assert key not in {
-                "secret_value",
-                "secret_values",
-                "resolved_secret",
-                "resolved_secrets",
-            }
-            _assert_no_secret_values(child)
-    elif isinstance(value, list):
-        for child in value:
-            _assert_no_secret_values(child)
-
-
 def test_scalar_udf_matches_shared_registration_golden_and_remains_callable():
    assert isinstance(normalize_score, UdfDefinition)
    assert normalize_score(25.0) == 0.25
@@ -67,13 +59,397 @@ def test_scalar_udf_matches_shared_registration_golden_and_remains_callable():
        "kind": "scalar_to_arrow_batch",
        "version": 1,
    }
-    assert request["required_secrets"] == ["API_TOKEN"]
-    _assert_no_secret_values(request)
+
+
+def _run_packaged(definition, *args):
+    """Execute the shipped artifact in a fresh namespace, as a worker would."""
+    source = base64.b64decode(definition.registration_request.artifact.content.data)
+    namespace: dict = {}
+    exec(compile(source, "<udf>", "exec"), namespace)
+    return namespace[definition.registration_request.artifact.entrypoint](*args)
+
+
+def test_udf_packages_attribute_access_and_body_imports():
+    @udf
+    def word_norm(body: str) -> float:
+        import numpy as np
+
+        try:
+            words = body.split()
+        except AttributeError as error:
+            raise ValueError(str(error)) from error
+        return float(np.linalg.norm([len(w) for w in words]))
+
+    assert _run_packaged(word_norm, "aa bb") == pytest.approx(8**0.5)
+
+
+def test_udf_packages_module_globals_and_global_caches():
+    @udf
+    def label(value: int) -> str:
+        return "big" if value >= THRESHOLD else "small"
+
+    assert _run_packaged(label, 21) == "big"
+
+    @udf
+    def cached(value: int) -> int:
+        global _CACHE
+        if _CACHE is None:
+            _CACHE = 40
+        return _CACHE + value
+
+    assert _run_packaged(cached, 2) == 42
+
+
+def test_udf_annotations_are_not_runtime_names():
+    @udf
+    def identity(value: date) -> date:
+        return value
+
+    assert _run_packaged(identity, date(2026, 8, 25)) == date(2026, 8, 25)
+
+
+def test_udf_nested_scopes_resolve_lexically():
+    @udf
+    def score(value: int) -> int:
+        offset = 2
+
+        def add_offset() -> int:
+            return value + offset
+
+        return add_offset() + sum(v for v in [0])
+
+    assert _run_packaged(score, 3) == 5
+
+
+def test_udf_resolves_module_globals_before_builtins(tmp_path):
+    module_path = tmp_path / "shadowing_udfs.py"
+    module_path.write_text(
+        "max = 7\n"
+        "len = lambda _: 99\n"
+        "\n"
+        "def uses_literal_shadow(value: int) -> int:\n"
+        "    def nested() -> int:\n"
+        "        return max\n"
+        "    return nested() + value\n"
+        "\n"
+        "def uses_callable_shadow(value: int) -> int:\n"
+        "    def nested() -> int:\n"
+        "        return len([1])\n"
+        "    return nested() + value\n"
+    )
+    spec = importlib.util.spec_from_file_location("shadowing_udfs", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+
+    # The module's `max = 7` is what the interpreter would use, so it ships.
+    assert _run_packaged(udf(module.uses_literal_shadow), 1) == 8
+    # A callable global cannot ship; it must not be silently swapped for the builtin.
+    with pytest.raises(TypeError, match="unsupported global value of type function"):
+        udf(module.uses_callable_shadow)
+
+
+def test_canonical_arrow_type_is_exactly_the_grammar():
+    from lancedb.functions import _GRAMMAR_PRIMITIVES, _canonical_arrow_type
+
+    golden = json.loads(
+        (
+            Path(__file__).parents[3]
+            / "rust/lancedb/tests/fixtures/first_class_functions/v1/arrow_types.json"
+        ).read_text()
+    )
+    primitives = [
+        case["arrow_type"] for case in golden["valid"] if "<" not in case["arrow_type"]
+    ]
+    assert [name for _, name in _GRAMMAR_PRIMITIVES] == primitives
+    for outside in [
+        pa.timestamp("us"),
+        pa.decimal128(10, 2),
+        pa.large_string(),
+        pa.large_binary(),
+        pa.binary(4),
+        pa.duration("s"),
+        pa.struct([pa.field("a", pa.int32())]),
+        pa.list_(pa.float32(), 0),
+        pa.list_(pa.timestamp("us")),
+    ]:
+        with pytest.raises(TypeError, match="unsupported Arrow type"):
+            _canonical_arrow_type(outside)
+
+
+def test_udf_nested_annotations_are_postponed_in_the_artifact():
+    @udf
+    def score(value: int) -> int:
+        def identity(item: date) -> date:
+            return item
+
+        identity(date(2026, 8, 25))
+        return value
+
+    assert _run_packaged(score, 3) == 3
+
+
+def test_udf_ships_globals_the_body_deletes():
+    @udf
+    def clear(value: int) -> int:
+        global _CACHE
+        del _CACHE
+        return value
+
+    assert _run_packaged(clear, 3) == 3
+
+
+def test_udf_rejects_a_module_global_that_does_not_import_as_itself(tmp_path):
+    module_path = tmp_path / "fake_module_udfs.py"
+    module_path.write_text(
+        "import types\n"
+        "np = types.ModuleType('numpy')\n"
+        "np.sqrt = lambda x: 0\n"
+        "\n"
+        "def score(value: int) -> int:\n"
+        "    return int(np.sqrt(value))\n"
+    )
+    spec = importlib.util.spec_from_file_location("fake_module_udfs", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    with pytest.raises(TypeError, match="does not import as 'numpy'"):
+        udf(module.score)
+
+
+def test_udf_rejects_a_module_level_namespace_alias(tmp_path):
+    module_path = tmp_path / "aliasing_udfs.py"
+    module_path.write_text(
+        "import builtins as b\n"
+        "THRESHOLD = 5\n"
+        "\n"
+        "def score(value: int) -> int:\n"
+        "    return value + b.vars(b.__import__('aliasing_udfs'))['THRESHOLD']\n"
+    )
+    spec = importlib.util.spec_from_file_location("aliasing_udfs", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    with pytest.raises(ValueError, match="dynamic namespace access"):
+        udf(module.score)
+
+
+@pytest.mark.parametrize(
+    "access",
+    [
+        "globals()['THRESHOLD']",
+        "eval('THRESHOLD')",
+        "(lambda g: g()['THRESHOLD'])(globals)",
+        "__import__('sys').modules[__name__].THRESHOLD",
+        "sys.modules[__name__].THRESHOLD",
+    ],
+)
+def test_udf_rejects_dynamic_namespace_access(access):
+    namespace: dict = {}
+    exec(
+        f"def score(value: int) -> int:\n    return value + {access}\n",
+        {"THRESHOLD": 5},
+        namespace,
+    )
+    with pytest.raises(ValueError, match="dynamic namespace access"):
+        _package_from_text(
+            "def score(value: int) -> int:\n"
+            "    import sys\n"
+            f"    return value + {access}\n"
+        )
+
+
+def _package_from_text(source: str, module_globals: dict | None = None):
+    """Load `source` as a real module file so the packager can inspect it."""
+    import tempfile
+
+    directory = tempfile.mkdtemp()
+    path = Path(directory) / "generated_udf_module.py"
+    path.write_text(source)
+    spec = importlib.util.spec_from_file_location(f"generated_udf_{id(source)}", path)
+    module = importlib.util.module_from_spec(spec)
+    if module_globals:
+        module.__dict__.update(module_globals)
+    spec.loader.exec_module(module)
+    functions = [
+        value
+        for value in vars(module).values()
+        if callable(value) and getattr(value, "__module__", None) == module.__name__
+    ]
+    return udf(functions[0])
+
+
+def test_udf_rejects_a_non_standard_builtins_environment():
+    def score(value: int) -> int:
+        return len([1]) + value
+
+    score.__globals__  # noqa: B018 -- real function, real globals
+    import builtins
+
+    patched = types.FunctionType(
+        score.__code__,
+        {"__builtins__": {**vars(builtins), "len": lambda _: 99}},
+        "score",
+    )
+    patched.__annotations__ = score.__annotations__
+    assert patched(3) == 102
+    with pytest.raises(ValueError, match="non-standard builtins environment"):
+        udf(patched)
+
+    class ReportingDict(dict):  # reports standard entries, resolves differently
+        def __missing__(self, key):
+            return vars(builtins)[key]
+
+    disguised = types.FunctionType(
+        score.__code__, {"__builtins__": ReportingDict(len=lambda _: 99)}, "score"
+    )
+    disguised.__annotations__ = score.__annotations__
+    assert disguised(3) == 102
+    with pytest.raises(ValueError, match="non-standard builtins environment"):
+        udf(disguised)
+
+    hooked = types.FunctionType(
+        score.__code__,
+        {"__builtins__": {**vars(builtins), "__import__": lambda *a, **k: None}},
+        "score",
+    )
+    hooked.__annotations__ = score.__annotations__
+    with pytest.raises(ValueError, match="non-standard builtins environment"):
+        udf(hooked)
+
+
+def test_udf_recursion_versus_a_rebound_module_name(tmp_path):
+    module_path = tmp_path / "rebound_udfs.py"
+    module_path.write_text(
+        "def fact(value: int) -> int:\n"
+        "    return 1 if value <= 1 else value * fact(value - 1)\n"
+        "\n"
+        "def score(value: int) -> int:\n"
+        "    return score + value\n"
+    )
+    spec = importlib.util.spec_from_file_location("rebound_udfs", module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    assert _run_packaged(udf(module.fact), 5) == 120
+    raw = module.score
+    module.score = 10
+    with pytest.raises(ValueError, match="binds that name to another value"):
+        udf(raw)
+    # A wrapper that merely exposes __wrapped__ is not the function.
+    module.score = functools.wraps(raw)(lambda value: 41)
+    with pytest.raises(ValueError, match="binds that name to another value"):
+        udf(raw)
+    # The decorator's own result is; a subclass of it is not.
+    module.fact = udf(module.fact)
+    assert _run_packaged(module.fact, 4) == 24
+
+    class Twisted(UdfDefinition):
+        def __call__(self, *args, **kwargs):
+            return 41
+
+    raw_fact = module.fact._function
+    module.fact = Twisted(
+        raw_fact,
+        name=None,
+        input_schema=None,
+        output_schema=None,
+        pip=(),
+        env={},
+        python_version=None,
+    )
+    with pytest.raises(ValueError, match="binds that name to another value"):
+        udf(raw_fact)
+
+
+def test_canonical_arrow_type_rejects_unrepresentable_list_children():
+    from lancedb.functions import _canonical_arrow_type
+
+    for outside in [
+        pa.list_(pa.float32()),  # pyarrow default: nullable child
+        pa.list_(pa.field("custom", pa.float32(), nullable=False)),
+        pa.list_(pa.field("item", pa.float32(), nullable=False, metadata={"k": "v"})),
+        pa.list_(pa.field("item", pa.float32(), nullable=False), 0),
+    ]:
+        with pytest.raises(TypeError, match="unsupported Arrow type"):
+            _canonical_arrow_type(outside)
+    assert (
+        _canonical_arrow_type(
+            pa.list_(pa.field("item", pa.float32(), nullable=False), 3)
+        )
+        == "fixed_size_list<float32, 3>"
+    )
+
+
+def _calls_missing(value: int) -> int:
+    return missing(value)  # noqa: F821
+
+
+def _shadows_missing_in_a_comprehension(value: int) -> int:
+    return missing(value) + sum(missing for missing in ())  # noqa: F821
+
+
+def _shadows_missing_in_a_lambda(value: int) -> int:
+    return (lambda missing: missing)(value) + missing  # noqa: F821
+
+
+@pytest.mark.parametrize(
+    "function",
+    [_calls_missing, _shadows_missing_in_a_comprehension, _shadows_missing_in_a_lambda],
+)
+def test_udf_rejects_a_truly_unresolved_global(function):
+    with pytest.raises(ValueError, match=r"unresolved global names: \['missing'\]"):
+        udf(function)
+
+
+def _arrow_type_from_golden(spec: dict) -> pa.DataType:
+    kind = spec["type"]
+    if kind in ("list", "large_list", "fixed_size_list"):
+        item = _arrow_type_from_golden(spec["fields"][0]["type"])
+        field = pa.field("item", item, nullable=False)
+        if kind == "list":
+            return pa.list_(field)
+        if kind == "large_list":
+            return pa.large_list(field)
+        return pa.list_(field, spec["length"])
+    return {
+        "null": pa.null(),
+        "bool": pa.bool_(),
+        "utf8": pa.string(),
+        "binary": pa.binary(),
+        "float16": pa.float16(),
+        "float32": pa.float32(),
+        "float64": pa.float64(),
+        "date32": pa.date32(),
+        "date64": pa.date64(),
+    }.get(kind) or getattr(pa, kind)()
+
+
+def test_arrow_type_grammar_matches_the_shared_golden():
+    golden = json.loads(
+        (
+            Path(__file__).parents[3]
+            / "rust/lancedb/tests/fixtures/first_class_functions/v1/arrow_types.json"
+        ).read_text()
+    )
+    from lancedb.functions import _canonical_arrow_type
+
+    emitted = {
+        case["arrow_type"]: _canonical_arrow_type(_arrow_type_from_golden(case["json"]))
+        for case in golden["valid"]
+    }
+    assert emitted == {
+        case["arrow_type"]: case["arrow_type"] for case in golden["valid"]
+    }
+    assert not set(emitted) & set(golden["invalid"])
+    for case in golden["server_only"]:
+        with pytest.raises(TypeError, match="unsupported Arrow type"):
+            _canonical_arrow_type(_arrow_type_from_golden(case["json"]))


 def test_explicit_arrow_schema_is_deterministic():
    input_schema = pa.schema([pa.field("value", pa.float32(), nullable=True)])
-    output_schema = pa.field("embedding", pa.list_(pa.float32(), 3), nullable=False)
+    output_schema = pa.field(
+        "embedding",
+        pa.list_(pa.field("item", pa.float32(), nullable=False), 3),
+        nullable=False,
+    )

    @udf(input_schema=input_schema, output_schema=output_schema)
    def explicit(value):
@@ -82,7 +458,7 @@ def test_explicit_arrow_schema_is_deterministic():
    signature = explicit.registration_request.signature
    assert signature.inputs[0].arrow_type == "float32"
    assert signature.inputs[0].nullable is True
-    assert signature.output.arrow_type == "fixed_size_list<float32>[3]"
+    assert signature.output.arrow_type == "fixed_size_list<float32, 3>"
    assert signature.output.nullable is False


@@ -130,14 +506,6 @@ def test_annotation_and_explicit_schema_validation_fail_closed():
            return value


-def test_environment_rejects_secret_value_overlap():
-    with pytest.raises(ValueError, match="must be disjoint"):
-
-        @udf(env={"TOKEN": "plaintext"}, secrets=["TOKEN"])
-        def overlapping(value: int) -> int:
-            return value
-
-
 def test_local_function_catalog_operations_are_not_supported(tmp_path):
    db = lancedb.connect(tmp_path)
    message = "Function catalog operations are not supported by this database"
@@ -174,7 +542,6 @@ def _mock_remote_function_catalog():
                    "runtime": body["runtime"],
                    "runtime_digest": "sha256:runtime",
                    "environment_digest": "sha256:environment",
-                    "required_secrets": body.get("required_secrets", []),
                    "created_at": "2026-08-21T00:00:00Z",
                }
                response = {"job_id": "job-register"}
@@ -233,7 +600,6 @@ def test_remote_registration_job_and_exact_version_reopen_round_trip():
    assert create_request == json.loads(
        normalize_score.registration_request.to_canonical_json()
    )
-    _assert_no_secret_values(create_request)


 def test_blocking_remote_registration_returns_function_version():
@@ -56,6 +56,31 @@ def test_execute_does_not_reenter_background_loop(tmp_path, monkeypatch):
    assert permutation_tbl._conn.read_consistency_interval is None


+def test_pickled_permutation_reads_pinned_version(tmp_path):
+    """An unpickled copy must still read the pinned version, which also covers the
+    version surviving the ``to_arrow()`` round trip in ``__getstate__``."""
+    import pickle
+
+    db = connect(tmp_path)
+    tbl = db.create_table("base", pa.table({"idx": range(20)}))
+    permutation_tbl = permutation_builder(tbl).execute()
+    perm = Permutation.from_tables(tbl, permutation_tbl)
+
+    payload = pickle.dumps(perm)
+
+    # Compact so the stored row addresses no longer describe these rows at latest.
+    tbl.delete("true")
+    tbl.optimize()
+    assert tbl.count_rows() == 0
+
+    # Unpickle after the mutation: __setstate__ reopens at latest, so this only
+    # passes if the recorded version is applied on reopen.
+    restored = pickle.loads(payload)
+    assert len(restored) == 20
+    rows = restored.__getitems__(list(range(20)))
+    assert sorted(row["idx"] for row in rows) == list(range(20))
+
+
 def test_split_random_counts(mem_db):
    """Test random splitting with absolute counts."""
    tbl = mem_db.create_table(
@@ -4,6 +4,7 @@

 import asyncio
 import copy
+from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
 import threading

@@ -86,6 +87,25 @@ def test_s3_lifecycle(s3_bucket: str):
    asyncio.run(test())


+@pytest.mark.s3_test
+def test_concurrent_open_table(s3_bucket: str):
+    uri = f"s3://{s3_bucket}/test_concurrent_open_table"
+    db = lancedb.connect(uri, storage_options=copy.copy(CONFIG))
+    db.create_table("test", pa.table({"x": [1, 2, 3]}))
+
+    num_workers = 32
+    barrier = threading.Barrier(num_workers)
+
+    def open_and_count(_):
+        barrier.wait()
+        return db.open_table("test").count_rows()
+
+    with ThreadPoolExecutor(max_workers=num_workers) as pool:
+        row_counts = list(pool.map(open_and_count, range(num_workers)))
+
+    assert row_counts == [3] * num_workers
+
+
@pytest.fixture()
 def kms_key():
    kms = get_boto3_client("kms", endpoint_url=CONFIG["aws_endpoint"])
@@ -780,15 +780,19 @@ impl Table {
        })
    }

-    #[pyo3(signature = (data, mode, progress=None, write_parallelism=None))]
+    #[pyo3(signature = (data, mode, progress=None, write_parallelism=None, allow_external_blob_outside_bases=false))]
    pub fn add<'a>(
        self_: PyRef<'a, Self>,
        data: PyScannable,
        mode: String,
        progress: Option<Py<PyAny>>,
        write_parallelism: Option<usize>,
+        allow_external_blob_outside_bases: bool,
    ) -> PyResult<Bound<'a, PyAny>> {
-        let mut op = self_.inner_ref()?.add(data);
+        let mut op = self_
+            .inner_ref()?
+            .add(data)
+            .allow_external_blob_outside_bases(allow_external_blob_outside_bases);
        if mode == "append" {
            op = op.mode(AddDataMode::Append);
        } else if mode == "overwrite" {
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.38.0-beta.6"
+version = "0.38.0-beta.10"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -1679,6 +1679,50 @@ mod tests {
        assert_eq!(tables, names[..7]);
    }

+    #[tokio::test]
+    async fn test_list_tables_walks_page_boundaries() {
+        let tc = new_test_connection().await.unwrap();
+        if tc.is_remote {
+            // What resumes a page is the server's to decide, and asserting it here would be
+            // asserting the server's contract rather than this one.
+            return;
+        }
+        let db = tc.connection;
+        let schema = Arc::new(Schema::new(vec![Field::new("x", DataType::Int32, false)]));
+        let mut names = Vec::with_capacity(5);
+        for _ in 0..5 {
+            let name = uuid::Uuid::new_v4().to_string();
+            names.push(name.clone());
+            db.create_empty_table(name, schema.clone())
+                .execute()
+                .await
+                .unwrap();
+        }
+        names.sort();
+
+        // Walking in pages has to reach every table exactly once, with nothing lost at a
+        // page boundary.
+        let mut seen = Vec::with_capacity(names.len());
+        let mut page_token = None;
+        loop {
+            let page = db
+                .list_tables(ListTablesRequest {
+                    id: Some(Vec::new()),
+                    limit: Some(2),
+                    page_token,
+                    ..Default::default()
+                })
+                .await
+                .unwrap();
+            seen.extend(page.tables);
+            page_token = page.page_token.filter(|token| !token.is_empty());
+            if page_token.is_none() {
+                break;
+            }
+        }
+        assert_eq!(seen, names);
+    }
+
    #[tokio::test]
    async fn test_open_table() {
        let tc = new_test_connection().await.unwrap();
@@ -974,17 +974,15 @@ impl Database for ListingDatabase {
            f.drain(0..index);
        }

-        // Determine if there's a next page
-        let next_page_token = if let Some(limit) = request.limit {
-            if f.len() > limit as usize {
-                let token = f[limit as usize].clone();
+        // Determine if there's a next page. The token is the last name of this page,
+        // not the first of the next one: the next page resumes strictly after the
+        // token, so naming the next page's first entry would skip it.
+        let next_page_token = match request.limit {
+            Some(limit) if f.len() > limit as usize => {
                f.truncate(limit as usize);
-                Some(token)
-            } else {
-                None
+                f.last().cloned()
            }
-        } else {
-            None
+            _ => None,
        };

        Ok(ListTablesResponse {
@@ -1478,7 +1476,7 @@ mod tests {
    use crate::table::{AnyQuery, WriteOptions};
    use arrow_array::{Int32Array, RecordBatch, StringArray};
    use arrow_schema::{DataType, Field, Schema, SchemaRef};
-    use futures::{TryStreamExt, stream::once};
+    use futures::{TryStreamExt, future::try_join_all, stream::once};
    use std::path::PathBuf;
    use std::sync::Arc;
    use std::time::Duration;
@@ -1616,6 +1614,59 @@ mod tests {
        );
    }

+    #[tokio::test]
+    async fn test_concurrent_open_table_reuses_connection_object_store() {
+        let tempdir = tempdir().unwrap();
+        let uri = tempdir.path().to_str().unwrap();
+        let session = Arc::new(lance::session::Session::default());
+        let request = ConnectRequest {
+            uri: uri.to_string(),
+            #[cfg(feature = "remote")]
+            client_config: Default::default(),
+            options: Default::default(),
+            namespace_client_properties: Default::default(),
+            manifest_enabled: false,
+            read_consistency_interval: None,
+            session: Some(session.clone()),
+        };
+        let db = ListingDatabase::connect_with_options(&request)
+            .await
+            .unwrap();
+        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
+        db.create_table(CreateTableRequest {
+            name: "test".to_string(),
+            namespace_path: vec![],
+            data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
+            mode: CreateTableMode::Create,
+            write_options: Default::default(),
+            location: None,
+            namespace_client: None,
+        })
+        .await
+        .unwrap();
+
+        let before = session.store_registry().stats();
+        let opened_tables = try_join_all((0..32).map(|_| {
+            db.open_table(OpenTableRequest {
+                name: "test".to_string(),
+                namespace_path: vec![],
+                index_cache_size: None,
+                lance_read_params: None,
+                location: None,
+                namespace_client: None,
+                managed_versioning: None,
+            })
+        }))
+        .await
+        .unwrap();
+        let after = session.store_registry().stats();
+
+        assert_eq!(opened_tables.len(), 32);
+        assert_eq!(after.misses, before.misses);
+        assert_eq!(after.active_stores, before.active_stores);
+        assert!(after.hits >= before.hits + 32);
+    }
+
    #[tokio::test]
    async fn test_listing_database_root_ops_do_not_create_manifest() {
        let tempdir = tempdir().unwrap();
@@ -27,6 +27,12 @@ pub const SRC_ROW_ID_COL: &str = "row_id";

 pub const SPLIT_NAMES_CONFIG_KEY: &str = "split_names";

+/// Base table version the permutation was built against.
+pub const BASE_VERSION_CONFIG_KEY: &str = "base_version";
+
+/// Base table branch the permutation was built against.  Absent means main.
+pub const BASE_BRANCH_CONFIG_KEY: &str = "base_branch";
+
 pub const DEFAULT_MEMORY_LIMIT: usize = 100 * 1024 * 1024;

 /// Where to store the permutation table
@@ -214,21 +220,11 @@ impl PermutationBuilder {
        Ok(Box::pin(SimpleRecordBatchStream { schema, stream }))
    }

-    fn add_split_names(
+    fn add_config_metadata(
        data: SendableRecordBatchStream,
-        split_names: &[String],
+        metadata: HashMap<String, String>,
    ) -> Result<SendableRecordBatchStream> {
-        let schema = data
-            .schema()
-            .as_ref()
-            .clone()
-            .with_metadata(HashMap::from([(
-                SPLIT_NAMES_CONFIG_KEY.to_string(),
-                serde_json::to_string(split_names).map_err(|e| Error::Other {
-                    message: format!("Failed to serialize split names: {}", e),
-                    source: Some(e.into()),
-                })?,
-            )]));
+        let schema = data.schema().as_ref().clone().with_metadata(metadata);
        let schema = Arc::new(schema);
        let schema_clone = schema.clone();
        let stream = data.map_ok(move |batch| batch.with_schema(schema.clone()).unwrap());
@@ -239,7 +235,20 @@ impl PermutationBuilder {
    }

    /// Builds the permutation table and stores it in the given database.
-    pub async fn build(self) -> Result<Table> {
+    pub async fn build(mut self) -> Result<Table> {
+        // Remote tables resolve latest independently for each request. Use a
+        // separate pinned handle so count, projection, and scan all refer to one
+        // snapshot without changing the caller's table checkout state. Native
+        // tables return `None` here and retain their existing behavior.
+        if let Some(snapshot) = self
+            .base_table
+            .base_table()
+            .snapshot_at_current_version()
+            .await?
+        {
+            self.base_table = Table::from(snapshot);
+        }
+
        // Unflushed rows have no row id, so a permutation cannot address them.
        match self.base_table.base_table().get_lsm_write_spec().await {
            Ok(Some(_)) => {
@@ -256,9 +265,14 @@ impl PermutationBuilder {
            Err(err) => return Err(err),
        }

+        // The handle above is already pinned to one version.  Record which one, so a
+        // reader -- in a DataLoader worker, against a table that has since moved --
+        // resolves these row addresses against the same snapshot.
+        let base_version = self.base_table.version().await?;
+        let base_branch = self.base_table.current_branch();
+
        // First pass, apply filter and load row ids.  `Shuffler` permutes positions, so
        // every rank must scan the rows in the same order to build the same permutation.
-        // TODO: pin the version resolved here; remote does not implement Lazy.
        let mut rows = self.base_table.query().select(Select::columns(&[ROW_ID]));

        if let Some(filter) = &self.config.filter {
@@ -318,11 +332,24 @@ impl PermutationBuilder {
        // Rename _rowid to row_id
        let renamed = rename_column(sorted, ROW_ID, SRC_ROW_ID_COL)?;

-        let streaming_data = if let Some(split_names) = &self.config.split_names {
-            Self::add_split_names(renamed, split_names)?
-        } else {
-            renamed
-        };
+        let mut metadata = HashMap::from([(
+            BASE_VERSION_CONFIG_KEY.to_string(),
+            base_version.to_string(),
+        )]);
+        // Version numbers are per-branch, so the branch is part of the coordinate.
+        if let Some(branch) = &base_branch {
+            metadata.insert(BASE_BRANCH_CONFIG_KEY.to_string(), branch.clone());
+        }
+        if let Some(split_names) = &self.config.split_names {
+            metadata.insert(
+                SPLIT_NAMES_CONFIG_KEY.to_string(),
+                serde_json::to_string(split_names).map_err(|e| Error::Other {
+                    message: format!("Failed to serialize split names: {}", e),
+                    source: Some(e.into()),
+                })?,
+            );
+        }
+        let streaming_data = Self::add_config_metadata(renamed, metadata)?;

        let (name, database) = match &self.config.destination {
            PermutationDestination::Permanent(database, table_name) => {
@@ -409,6 +436,253 @@ mod tests {
        assert!(table.base_table().scan_order_is_deterministic());
    }

+    #[cfg(feature = "remote")]
+    #[tokio::test]
+    async fn test_remote_permutation_builder_pins_snapshot() {
+        use std::sync::{
+            Mutex,
+            atomic::{AtomicU64, Ordering},
+        };
+
+        use arrow_array::{RecordBatch, UInt64Array};
+        use arrow_schema::{DataType, Field, Schema};
+
+        let row_ids = RecordBatch::try_new(
+            Arc::new(Schema::new(vec![Field::new(
+                ROW_ID,
+                DataType::UInt64,
+                false,
+            )])),
+            vec![Arc::new(UInt64Array::from(vec![100]))],
+        )
+        .unwrap();
+        let mut query_body = Vec::new();
+        {
+            let mut writer =
+                arrow_ipc::writer::FileWriter::try_new(&mut query_body, &row_ids.schema()).unwrap();
+            writer.write(&row_ids).unwrap();
+            writer.finish().unwrap();
+        }
+
+        let latest = Arc::new(AtomicU64::new(7));
+        let expected_snapshot = Arc::new(AtomicU64::new(7));
+        let planning_versions = Arc::new(Mutex::new(Vec::new()));
+        let latest_ref = latest.clone();
+        let expected_snapshot_ref = expected_snapshot.clone();
+        let planning_versions_ref = planning_versions.clone();
+        let table = Table::new_with_handler("remote_base", move |request| {
+            let path = request.url().path();
+            let body = request
+                .body()
+                .and_then(|body| body.as_bytes())
+                .map(|body| serde_json::from_slice::<serde_json::Value>(body).unwrap());
+
+            match path {
+                "/v1/table/remote_base/describe/" => {
+                    let requested = body.as_ref().and_then(|body| body["version"].as_u64());
+                    let version = requested.unwrap_or_else(|| latest_ref.load(Ordering::SeqCst));
+                    http::Response::builder()
+                        .status(200)
+                        .body(
+                            format!(r#"{{"version":{version},"schema":{{"fields":[]}}}}"#)
+                                .into_bytes(),
+                        )
+                        .unwrap()
+                }
+                "/v1/table/remote_base/get_lsm_write_spec/" => http::Response::builder()
+                    .status(200)
+                    .body(br#"{"lsm_write_spec":null}"#.to_vec())
+                    .unwrap(),
+                "/v1/table/remote_base/count_rows/" => {
+                    let body = body.unwrap();
+                    let version = body["version"].as_u64().unwrap();
+                    assert_eq!(version, expected_snapshot_ref.load(Ordering::SeqCst));
+                    assert_eq!(body["predicate"], "value > 0");
+                    planning_versions_ref.lock().unwrap().push(version);
+
+                    // Simulate a concurrent append after count_rows. An unpinned
+                    // scan would now resolve version 8 and include different rows.
+                    latest_ref.store(8, Ordering::SeqCst);
+                    http::Response::builder()
+                        .status(200)
+                        .body(b"1".to_vec())
+                        .unwrap()
+                }
+                "/v1/table/remote_base/query/" => {
+                    let body = body.unwrap();
+                    let version = body["version"].as_u64().unwrap();
+                    assert_eq!(version, expected_snapshot_ref.load(Ordering::SeqCst));
+                    assert_eq!(body["filter"], "value > 0");
+                    assert_eq!(body["columns"], serde_json::json!([ROW_ID]));
+                    planning_versions_ref.lock().unwrap().push(version);
+                    http::Response::builder()
+                        .status(200)
+                        .header("content-type", "application/vnd.apache.arrow.file")
+                        .body(query_body.clone())
+                        .unwrap()
+                }
+                _ => panic!("unexpected request: {path}"),
+            }
+        });
+
+        let permutation = PermutationBuilder::new(table.clone())
+            .with_filter("value > 0".to_string())
+            .build()
+            .await
+            .unwrap();
+        assert_eq!(permutation.count_rows(None).await.unwrap(), 1);
+
+        // Building uses a separate handle and must not pin the caller's table.
+        assert_eq!(table.version().await.unwrap(), 8);
+
+        // An explicit checkout is copied as-is and remains checked out afterward.
+        expected_snapshot.store(6, Ordering::SeqCst);
+        table.checkout(6).await.unwrap();
+        let permutation = PermutationBuilder::new(table.clone())
+            .with_filter("value > 0".to_string())
+            .build()
+            .await
+            .unwrap();
+        assert_eq!(permutation.count_rows(None).await.unwrap(), 1);
+        assert_eq!(table.version().await.unwrap(), 6);
+        assert_eq!(*planning_versions.lock().unwrap(), vec![7, 7, 6, 6]);
+    }
+
+    #[tokio::test]
+    async fn test_permutation_records_base_version() {
+        let temp_dir = tempfile::tempdir().unwrap();
+
+        let db = connect(temp_dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+
+        let initial_data = lance_datagen::gen_batch()
+            .col("col_a", lance_datagen::array::step::<Int32Type>())
+            .into_ldb_stream(RowCount::from(100), BatchCount::from(2));
+        let data_table = db
+            .create_table("base_tbl", initial_data)
+            .execute()
+            .await
+            .unwrap();
+
+        let build_version = data_table.version().await.unwrap();
+        let permutation_table = PermutationBuilder::new(data_table.clone())
+            .build()
+            .await
+            .unwrap();
+
+        let recorded = permutation_table
+            .schema()
+            .await
+            .unwrap()
+            .metadata
+            .get(BASE_VERSION_CONFIG_KEY)
+            .expect("permutation should record the base version")
+            .parse::<u64>()
+            .unwrap();
+        assert_eq!(recorded, build_version);
+
+        // Advancing the base table must not move the recorded version.
+        let more_data = lance_datagen::gen_batch()
+            .col("col_a", lance_datagen::array::step::<Int32Type>())
+            .into_ldb_stream(RowCount::from(50), BatchCount::from(1));
+        data_table.add(more_data).execute().await.unwrap();
+        assert!(data_table.version().await.unwrap() > recorded);
+        assert_eq!(
+            permutation_table
+                .schema()
+                .await
+                .unwrap()
+                .metadata
+                .get(BASE_VERSION_CONFIG_KEY)
+                .unwrap()
+                .parse::<u64>()
+                .unwrap(),
+            recorded,
+        );
+    }
+
+    /// Version numbers are per-branch, so a permutation built on a branch must record
+    /// it -- a worker reopens by name and lands on main at the same number.
+    #[tokio::test]
+    async fn test_permutation_records_base_branch() {
+        let temp_dir = tempfile::tempdir().unwrap();
+        let db = connect(temp_dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+
+        let initial_data = lance_datagen::gen_batch()
+            .col("col_a", lance_datagen::array::step::<Int32Type>())
+            .into_ldb_stream(RowCount::from(10), BatchCount::from(1));
+        let data_table = db
+            .create_table("base_tbl", initial_data)
+            .execute()
+            .await
+            .unwrap();
+
+        let branch = data_table
+            .create_branch("exp", lance::dataset::refs::Ref::from(("main", 1)))
+            .await
+            .unwrap();
+        let permutation_table = PermutationBuilder::new(branch.clone())
+            .build()
+            .await
+            .unwrap();
+
+        let metadata = permutation_table.schema().await.unwrap().metadata.clone();
+        assert_eq!(
+            metadata.get(BASE_BRANCH_CONFIG_KEY).map(String::as_str),
+            Some("exp")
+        );
+
+        // Main records nothing, so an absent key keeps meaning main.
+        let main_permutation = PermutationBuilder::new(data_table.clone())
+            .build()
+            .await
+            .unwrap();
+        assert!(
+            !main_permutation
+                .schema()
+                .await
+                .unwrap()
+                .metadata
+                .contains_key(BASE_BRANCH_CONFIG_KEY)
+        );
+    }
+
+    #[tokio::test]
+    async fn test_build_does_not_pin_the_callers_table() {
+        let temp_dir = tempfile::tempdir().unwrap();
+
+        let db = connect(temp_dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+
+        let initial_data = lance_datagen::gen_batch()
+            .col("col_a", lance_datagen::array::step::<Int32Type>())
+            .into_ldb_stream(RowCount::from(100), BatchCount::from(1));
+        let data_table = db
+            .create_table("base_tbl", initial_data)
+            .execute()
+            .await
+            .unwrap();
+
+        PermutationBuilder::new(data_table.clone())
+            .build()
+            .await
+            .unwrap();
+
+        // The builder pins its own handle; the caller's must still track latest.
+        let more_data = lance_datagen::gen_batch()
+            .col("col_a", lance_datagen::array::step::<Int32Type>())
+            .into_ldb_stream(RowCount::from(50), BatchCount::from(1));
+        data_table.add(more_data).execute().await.unwrap();
+        assert_eq!(data_table.count_rows(None).await.unwrap(), 150);
+    }
+
    #[tokio::test]
    async fn test_permutation_builder() {
        let temp_dir = tempfile::tempdir().unwrap();
@@ -8,7 +8,9 @@
 //! the rows from a source table that correspond to row IDs stored in a separate table.

 use crate::arrow::{SendableRecordBatchStream, SimpleRecordBatchStream};
-use crate::dataloader::permutation::builder::SRC_ROW_ID_COL;
+use crate::dataloader::permutation::builder::{
+    BASE_BRANCH_CONFIG_KEY, BASE_VERSION_CONFIG_KEY, SRC_ROW_ID_COL,
+};
 use crate::dataloader::permutation::split::SPLIT_ID_COLUMN;
 use crate::error::Error;
 use crate::query::{
@@ -23,6 +25,7 @@ use arrow_array::{RecordBatch, UInt64Array};
 use arrow_schema::SchemaRef;
 use datafusion_expr::{Expr, col, lit};
 use futures::{StreamExt, TryStreamExt};
+use lance::dataset::refs::MAIN_BRANCH;
 use lance::dataset::scanner::DatasetRecordBatchStream;
 use lance::io::RecordBatchStream;
 use lance_arrow::RecordBatchExt;
@@ -69,6 +72,10 @@ impl PermutationReader {
        permutation_table: Option<Arc<dyn BaseTable>>,
        split: u64,
    ) -> Result<Self> {
+        let base_table = match &permutation_table {
+            Some(permutation_table) => Self::pin_base_table(base_table, permutation_table).await?,
+            None => base_table,
+        };
        let mut slf = Self {
            base_table,
            permutation_table,
@@ -89,6 +96,34 @@ impl PermutationReader {
        Ok(slf)
    }

+    /// Pins the base table to the version the permutation was built against.
+    /// Permutations written before that was recorded carry no key and stay unpinned.
+    async fn pin_base_table(
+        base_table: Arc<dyn BaseTable>,
+        permutation_table: &Arc<dyn BaseTable>,
+    ) -> Result<Arc<dyn BaseTable>> {
+        let schema = permutation_table.schema().await?;
+        let Some(raw) = schema.metadata.get(BASE_VERSION_CONFIG_KEY) else {
+            return Ok(base_table);
+        };
+        let version = raw.parse::<u64>().map_err(|e| Error::InvalidInput {
+            message: format!(
+                "Permutation table has an unreadable {} of {:?}: {}",
+                BASE_VERSION_CONFIG_KEY, raw, e
+            ),
+        })?;
+        // The recorded branch, not the handle's: a worker reopens by name and lands
+        // on main, and version numbers are per-branch.
+        let branch = schema
+            .metadata
+            .get(BASE_BRANCH_CONFIG_KEY)
+            .map(String::as_str)
+            .unwrap_or(MAIN_BRANCH);
+        base_table
+            .checkout_branch_version(branch, Some(version))
+            .await
+    }
+
    pub async fn try_from_tables(
        base_table: Arc<dyn BaseTable>,
        permutation_table: Arc<dyn BaseTable>,
@@ -511,9 +546,13 @@ mod tests {
    use lance_datagen::{BatchCount, RowCount};
    use rand::seq::SliceRandom;

+    // Aliased: `test_utils::datagen` exports a trait of the same name.
+    use crate::arrow::LanceDbDatagenExt as _;
    use crate::{
        Table,
        arrow::SendableRecordBatchStream,
+        connect,
+        dataloader::permutation::builder::PermutationBuilder,
        query::{ExecutableQuery, QueryBase},
        test_utils::datagen::{LanceDbDatagenExt, virtual_table},
    };
@@ -545,6 +584,58 @@ mod tests {
        .await
    }

+    /// Compaction moves row addresses, so the reader must read the pinned version.
+    #[tokio::test]
+    async fn test_reader_pins_base_version() {
+        let temp_dir = tempfile::tempdir().unwrap();
+        let db = connect(temp_dir.path().to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+
+        let data = lance_datagen::gen_batch()
+            .col("idx", lance_datagen::array::step::<Int32Type>())
+            .into_ldb_stream(RowCount::from(20), BatchCount::from(1));
+        let base_table = db.create_table("base_tbl", data).execute().await.unwrap();
+
+        let permutation_table = PermutationBuilder::new(base_table.clone())
+            .build()
+            .await
+            .unwrap();
+
+        base_table.delete("true").await.unwrap();
+        base_table
+            .optimize(crate::table::OptimizeAction::All)
+            .await
+            .unwrap();
+        assert_eq!(base_table.count_rows(None).await.unwrap(), 0);
+
+        let reader = PermutationReader::try_from_tables(
+            base_table.base_table().clone(),
+            permutation_table.base_table().clone(),
+            0,
+        )
+        .await
+        .unwrap();
+
+        let values = collect_from_stream::<Int32Type>(
+            reader
+                .read(
+                    Select::Columns(vec!["idx".to_string()]),
+                    QueryExecutionOptions::default(),
+                )
+                .await
+                .unwrap(),
+            "idx",
+        )
+        .await;
+        assert_eq!(
+            values.len(),
+            20,
+            "reader should still see the pinned version"
+        );
+    }
+
    #[tokio::test]
    async fn test_permutation_reader() {
        let base_table = lance_datagen::gen_batch()
@@ -5,7 +5,7 @@
 //! backend-neutral terminal result of a computed-column refresh.
 //!
 //! This module contains client/wire values only. Catalog persistence,
-//! environment bake, secret resolution, and execution are owned by Sophon.
+//! environment bake, and execution are owned by Sophon.

 use std::collections::BTreeMap;

@@ -195,9 +195,6 @@ pub struct PythonEnvironmentSpec {
 }

 /// Reproducible Python runtime definition understood by Sophon.
-///
-/// `env` contains non-secret values. Secret values have no client model;
-/// [`FunctionVersion::required_secrets`] contains names only.
 #[derive(Debug, Clone, PartialEq, Eq)]
 #[non_exhaustive]
 pub enum PythonRuntimeSpec {
@@ -239,7 +236,7 @@ impl PythonRuntimeSpec {
        }
    }

-    /// Non-secret environment variables, or `None` for an unknown kind.
+    /// Environment variables, or `None` for an unknown kind.
    pub fn env(&self) -> Option<&BTreeMap<String, String>> {
        match self {
            Self::Python { env, .. } => Some(env),
@@ -324,8 +321,6 @@ pub struct FunctionVersion {
    runtime: PythonRuntimeSpec,
    runtime_digest: String,
    environment_digest: String,
-    #[serde(default, skip_serializing_if = "Vec::is_empty")]
-    required_secrets: Vec<String>,
    created_at: String,
 }

@@ -358,11 +353,6 @@ impl FunctionVersion {
        &self.environment_digest
    }

-    /// Required secret names. Resolved values exist only inside Sophon.
-    pub fn required_secrets(&self) -> &[String] {
-        &self.required_secrets
-    }
-
    pub fn created_at(&self) -> &str {
        &self.created_at
    }
@@ -404,18 +394,12 @@ pub struct FunctionArtifactRequest {
 }

 /// Stable request envelope for remote immutable Function registration.
-///
-/// Secret values deliberately have no field in this model. The only secret
-/// material the client may send is the ordered set of names Sophon resolves
-/// inside the remote runtime.
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
 pub struct FunctionRegistrationRequest {
    pub name: String,
    pub artifact: FunctionArtifactRequest,
    pub signature: FunctionSignature,
    pub runtime: PythonRuntimeSpec,
-    #[serde(default, skip_serializing_if = "Vec::is_empty")]
-    pub required_secrets: Vec<String>,
 }

 impl_json!(FunctionRegistrationRequest);
@@ -446,7 +430,6 @@ pub struct FunctionApplication {
    function: FunctionVersionRef,
    inputs: Vec<ApplicationInput>,
    output: FunctionOutput,
-    group_id: String,
    #[serde(default, skip_serializing_if = "BTreeMap::is_empty")]
    columns: BTreeMap<String, String>,
    #[serde(default, flatten, skip_serializing)]
@@ -468,10 +451,6 @@ impl FunctionApplication {
        &self.output
    }

-    pub fn group_id(&self) -> &str {
-        &self.group_id
-    }
-
    pub fn columns(&self) -> &BTreeMap<String, String> {
        &self.columns
    }
@@ -513,7 +492,7 @@ pub struct InputBinding {
    pub nullable: bool,
 }

-/// Ordered result-field to table-field mapping for a grouped binding.
+/// Ordered result-field to table-field mapping for a Function binding.
 ///
 /// Assignment state is not part of the Slice 1 client contract. During the
 /// NULL transition there is no public Lance cell-flag identifier to persist.
@@ -527,20 +506,18 @@ pub struct OutputMapping {
    pub nullable: bool,
 }

-/// Immutable grouped binding persisted by the Enterprise table service.
+/// Immutable Function binding persisted by the Enterprise table service.
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
 pub struct FunctionBinding {
    binding_id: String,
-    revision: u64,
    function: FunctionVersionRef,
-    group_id: String,
    inputs: Vec<InputBinding>,
    outputs: Vec<OutputMapping>,
    /// Exact Arrow schema presented to the Function, encoded with the Lance
    /// Namespace Arrow JSON representation.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    input_schema: Option<Value>,
-    /// Exact physical Arrow schema of the grouped table outputs.
+    /// Exact physical Arrow schema of the binding's table outputs.
    #[serde(default, skip_serializing_if = "Option::is_none")]
    output_schema: Option<Value>,
 }
@@ -550,18 +527,10 @@ impl FunctionBinding {
        &self.binding_id
    }

-    pub fn revision(&self) -> u64 {
-        self.revision
-    }
-
    pub fn function(&self) -> &FunctionVersionRef {
        &self.function
    }

-    pub fn group_id(&self) -> &str {
-        &self.group_id
-    }
-
    pub fn inputs(&self) -> &[InputBinding] {
        &self.inputs
    }
@@ -10,7 +10,7 @@ use lance::io::WrappingObjectStore;
 use object_store::{
    CopyOptions, Error, GetOptions, GetResult, ListResult, MultipartUpload, ObjectMeta,
    ObjectStore, ObjectStoreExt, PutMultipartOptions, PutOptions, PutPayload, PutResult, Result,
-    UploadPart, path::Path,
+    UploadPart, list::PaginatedListStore, path::Path,
 };

 use async_trait::async_trait;
@@ -187,6 +187,14 @@ impl WrappingObjectStore for MirroringObjectStoreWrapper {
            secondary: self.secondary.clone(),
        })
    }
+
+    fn wrap_paginated(
+        &self,
+        _store_prefix: &str,
+        original: Arc<dyn PaginatedListStore>,
+    ) -> Option<Arc<dyn PaginatedListStore>> {
+        Some(original)
+    }
 }

 // windows pathing can't be simply concatenated
@@ -12,7 +12,7 @@ use lance::io::WrappingObjectStore;
 use object_store::{
    CopyOptions, GetOptions, GetResult, ListResult, MultipartUpload, ObjectMeta, ObjectStore,
    PutMultipartOptions, PutOptions, PutPayload, PutResult, RenameOptions, Result as OSResult,
-    UploadPart, path::Path,
+    UploadPart, list::PaginatedListStore, path::Path,
 };

 #[derive(Debug, Default)]
@@ -57,6 +57,14 @@ impl WrappingObjectStore for IoStatsHolder {
            stats: self.0.clone(),
        })
    }
+
+    fn wrap_paginated(
+        &self,
+        _store_prefix: &str,
+        original: Arc<dyn PaginatedListStore>,
+    ) -> Option<Arc<dyn PaginatedListStore>> {
+        Some(original)
+    }
 }

 impl IoTrackingStore {
@@ -47,6 +47,10 @@ impl TerminalResult {
        }
    }

+    pub(crate) fn value(&self) -> Option<&Value> {
+        self.value.as_ref()
+    }
+
    fn decode<T: DeserializeOwned>(self) -> Result<T> {
        let value = self.value.ok_or_else(|| match &self.request_id {
            Some(request_id) => Error::Http {
@@ -37,6 +37,13 @@ pub use refresh::{RefreshMaterializedViewResult, RefreshMode};
 /// Schema metadata key holding the view definition, as kind-tagged JSON.
 pub const DEFINITION_META_KEY: &str = "mv.definition";

+/// Schema metadata key holding the view's incarnation: a token minted at each
+/// physical creation of a view table, so a view dropped and recreated under
+/// the same name and definition is still told apart from the one a caller
+/// captured. A view whose metadata was replaced wholesale, or one declared
+/// before tokens existed, carries none until its next refresh mints one.
+pub const INCARNATION_META_KEY: &str = "mv.incarnation";
+
 /// Schema metadata key holding the source table version the view was last
 /// refreshed to. Absent until the first refresh.
 pub const SOURCE_VERSION_META_KEY: &str = "mv.source_version";
@@ -612,8 +619,17 @@ impl PreparedDeclaration {
    pub async fn create(self, name: &str) -> Result<MaterializedView> {
        let empty: Vec<std::result::Result<arrow_array::RecordBatch, arrow_schema::ArrowError>> =
            vec![];
+        // Minted here, not at preparation: a declaration can be cloned and
+        // create more than one physical table, and each needs its own token.
+        let incarnation = uuid::Uuid::new_v4().to_string();
+        let mut metadata = self.schema.metadata().clone();
+        metadata.insert(INCARNATION_META_KEY.to_string(), incarnation.clone());
+        let schema = Arc::new(ArrowSchema::new_with_metadata(
+            self.schema.fields().clone(),
+            metadata,
+        ));
        let reader: Box<dyn arrow_array::RecordBatchReader + Send> =
-            Box::new(arrow_array::RecordBatchIterator::new(empty, self.schema));
+            Box::new(arrow_array::RecordBatchIterator::new(empty, schema));
        let mut request = CreateTableRequest::new(name.to_string(), Box::new(reader));
        let write_params = request
            .write_options
@@ -648,6 +664,7 @@ impl PreparedDeclaration {
        Ok(MaterializedView {
            table,
            definition: self.definition,
+            incarnation: Some(incarnation),
        })
    }
 }
@@ -878,6 +895,7 @@ impl CreateMaterializedViewBuilder {
 pub struct MaterializedView {
    table: Table,
    definition: MaterializedViewDefinition,
+    incarnation: Option<String>,
 }

 impl MaterializedView {
@@ -893,8 +911,13 @@ impl MaterializedView {
            });
        }
        let schema = table.schema().await?;
+        let incarnation = schema.metadata().get(INCARNATION_META_KEY).cloned();
        match materialized_view_kind(schema.metadata())? {
-            Some(MaterializedViewKind::Select(definition)) => Ok(Self { table, definition }),
+            Some(MaterializedViewKind::Select(definition)) => Ok(Self {
+                table,
+                definition,
+                incarnation,
+            }),
            Some(MaterializedViewKind::Unrecognized { kind }) => Err(Error::NotSupported {
                message: format!(
                    "materialized view '{}' is defined by '{kind}', which this version of \
@@ -923,6 +946,13 @@ impl MaterializedView {
        &self.definition
    }

+    /// The view's incarnation token as of when this handle was opened; see
+    /// [`RefreshMaterializedViewBuilder::expect_incarnation`]. `None` for a
+    /// view that has none yet (see [`INCARNATION_META_KEY`]).
+    pub fn incarnation(&self) -> Option<&str> {
+        self.incarnation.as_deref()
+    }
+
    /// Recompute the view from its source.
    ///
    /// By default the refresh is incremental when the source's changes can be
@@ -943,6 +973,7 @@ impl MaterializedView {
            view: self.clone(),
            full: false,
            source_version: None,
+            expected_incarnation: None,
        }
    }
 }
@@ -952,6 +983,7 @@ pub struct RefreshMaterializedViewBuilder {
    view: MaterializedView,
    full: bool,
    source_version: Option<u64>,
+    expected_incarnation: Option<String>,
 }

 impl RefreshMaterializedViewBuilder {
@@ -967,8 +999,28 @@ impl RefreshMaterializedViewBuilder {
        self
    }

+    /// Refresh only if the view is still the incarnation that minted `token`
+    /// (see [`MaterializedView::incarnation`]): a refresh requested against
+    /// one declaration must not land in a view dropped and recreated since,
+    /// even under the same name and definition.
+    ///
+    /// Best effort. The token is read from the latest stored manifest before
+    /// planning and again immediately before every commit, but it is not part
+    /// of the commit's own condition, so a recreation that lands between that
+    /// final read and the commit is not caught.
+    pub fn expect_incarnation(mut self, token: impl Into<String>) -> Self {
+        self.expected_incarnation = Some(token.into());
+        self
+    }
+
    pub async fn execute(self) -> Result<RefreshMaterializedViewResult> {
-        refresh::execute_refresh(&self.view.table, self.full, self.source_version).await
+        refresh::execute_refresh(
+            &self.view.table,
+            self.full,
+            self.source_version,
+            self.expected_incarnation.as_deref(),
+        )
+        .await
    }
 }

@@ -46,8 +46,8 @@ use lance_table::format::Fragment;
 use serde::{Deserialize, Serialize};

 use super::{
-    MaterializedViewDefinition, REFRESHED_AT_MS_META_KEY, SOURCE_ROW_ID_COLUMN,
-    SOURCE_VERSION_META_KEY,
+    INCARNATION_META_KEY, MaterializedViewDefinition, REFRESHED_AT_MS_META_KEY,
+    SOURCE_ROW_ID_COLUMN, SOURCE_VERSION_META_KEY,
 };
 use crate::database::OpenTableRequest;
 use crate::table::{NativeTable, NativeTableExt, Table};
@@ -108,6 +108,7 @@ pub(crate) async fn execute_refresh(
    view: &Table,
    full: bool,
    pinned: Option<u64>,
+    expected_incarnation: Option<&str>,
 ) -> Result<RefreshMaterializedViewResult> {
    let view_native = view.as_native().ok_or_else(|| Error::NotSupported {
        message: "materialized views are supported only on local tables".into(),
@@ -122,6 +123,8 @@ pub(crate) async fn execute_refresh(
    view_native.dataset.reload().await?;
    let view_ds = view_native.dataset.get().await?.as_ref().clone();

+    ensure_incarnation(&view_ds, expected_incarnation, view.name()).await?;
+
    // The definition a handle cached at open may since have been replaced;
    // what refresh executes and what it stamps must be one generation.
    let definition = match super::materialized_view_kind(&view_ds.schema().metadata)? {
@@ -240,6 +243,7 @@ pub(crate) async fn execute_refresh(
                increment,
                definition,
                watermark,
+                expected_incarnation,
            )
            .await?;
            match reconciled {
@@ -253,6 +257,7 @@ pub(crate) async fn execute_refresh(
                        source_version,
                        source_ts,
                        definition,
+                        expected_incarnation,
                    )
                    .await
                }
@@ -266,6 +271,7 @@ pub(crate) async fn execute_refresh(
                source_version,
                source_ts,
                definition,
+                expected_incarnation,
            )
            .await
        }
@@ -595,6 +601,7 @@ async fn incremental(
    increment: Increment,
    definition: &MaterializedViewDefinition,
    watermark: Option<u64>,
+    expected_incarnation: Option<&str>,
 ) -> Result<Option<RefreshMaterializedViewResult>> {
    let new_fragments = increment.appended;
    let watermark_version = watermark.unwrap_or(0);
@@ -671,15 +678,35 @@ async fn incremental(
    };
    let nothing_to_add = (new_fragments.is_empty() && !updated_rows) || remaining == Some(0);
    if nothing_to_add && eviction.is_none() {
-        result.version =
-            stamp_watermark(view_native, view_ds.clone(), source_version, source_ts).await?;
+        result.version = stamp_watermark(
+            view_native,
+            view_ds.clone(),
+            source_version,
+            source_ts,
+            expected_incarnation,
+        )
+        .await?;
        return Ok(Some(result));
    }
    // Rows left but none arrive: the removals still have to be published.
    if nothing_to_add {
        let filter = refresh_filter(&empty_keys(view_ds)?)?;
-        let published = publish(view_ds, eviction, Vec::new(), Some(filter)).await?;
-        result.version = stamp_watermark(view_native, published, source_version, source_ts).await?;
+        let published = publish(
+            view_ds,
+            eviction,
+            Vec::new(),
+            Some(filter),
+            expected_incarnation,
+        )
+        .await?;
+        result.version = stamp_watermark(
+            view_native,
+            published,
+            source_version,
+            source_ts,
+            expected_incarnation,
+        )
+        .await?;
        return Ok(Some(result));
    }

@@ -737,12 +764,20 @@ async fn incremental(
                eviction,
                Vec::new(),
                Some(refresh_filter(&empty_keys(view_ds)?)?),
+                expected_incarnation,
            )
            .await?
        } else {
            view_ds.clone()
        };
-        result.version = stamp_watermark(view_native, published, source_version, source_ts).await?;
+        result.version = stamp_watermark(
+            view_native,
+            published,
+            source_version,
+            source_ts,
+            expected_incarnation,
+        )
+        .await?;
        return Ok(Some(result));
    };
    let stream: SendableRecordBatchStream = Box::pin(RecordBatchStreamAdapter::new(
@@ -775,9 +810,23 @@ async fn incremental(
        });
    };
    let filter = refresh_filter(&keys)?;
-    let appended = publish(view_ds, eviction, new_fragments, Some(filter)).await?;
+    let appended = publish(
+        view_ds,
+        eviction,
+        new_fragments,
+        Some(filter),
+        expected_incarnation,
+    )
+    .await?;
    result.rows_written = rows_written.load(Ordering::Relaxed);
-    result.version = stamp_watermark(view_native, appended, source_version, source_ts).await?;
+    result.version = stamp_watermark(
+        view_native,
+        appended,
+        source_version,
+        source_ts,
+        expected_incarnation,
+    )
+    .await?;
    Ok(Some(result))
 }

@@ -788,6 +837,7 @@ async fn rebuild(
    source_version: u64,
    source_ts: u128,
    definition: &MaterializedViewDefinition,
+    expected_incarnation: Option<&str>,
 ) -> Result<RefreshMaterializedViewResult> {
    let rows_written = Arc::new(AtomicU64::new(0));
    let schema = Arc::new(ArrowSchema::from(view_ds.schema()));
@@ -810,8 +860,16 @@ async fn rebuild(
    // carries no schema metadata, so it cannot erase a definition update
    // that raced in the way an overwrite (which adopts its stream's schema)
    // durably would -- and it must land on the planned generation or abort.
-    let replaced = replace_retaining_indices(view_ds.clone(), stream, keys).await?;
-    let version = stamp_watermark(view_native, replaced, source_version, source_ts).await?;
+    let replaced =
+        replace_retaining_indices(view_ds.clone(), stream, keys, expected_incarnation).await?;
+    let version = stamp_watermark(
+        view_native,
+        replaced,
+        source_version,
+        source_ts,
+        expected_incarnation,
+    )
+    .await?;
    Ok(RefreshMaterializedViewResult {
        mode: RefreshMode::Rebuild,
        rows_written: rows_written.load(Ordering::Relaxed),
@@ -828,11 +886,13 @@ async fn replace_retaining_indices(
    view_ds: Dataset,
    stream: SendableRecordBatchStream,
    keys: Arc<StdMutex<KeyExistenceFilterBuilder>>,
+    expected_incarnation: Option<&str>,
 ) -> Result<Dataset> {
    let ds = Arc::new(view_ds);
    let read_version = ds.version().version;
    #[cfg(test)]
    tests::hold_before_publish(ds.uri()).await;
+    ensure_incarnation(&ds, expected_incarnation, ds.uri()).await?;
    let removed_fragment_ids: Vec<u64> = ds.get_fragments().iter().map(|f| f.id() as u64).collect();

    let write_txn = InsertBuilder::new(WriteDestination::Dataset(ds.clone()))
@@ -886,6 +946,32 @@ async fn replace_retaining_indices(
 }

 /// Record that the view now reflects `source_version`, including the view
+/// Refuse to act on a view that is not `expected`'s incarnation, judged from
+/// the latest stored manifest. Not a commit condition; see
+/// `RefreshMaterializedViewBuilder::expect_incarnation`.
+async fn ensure_incarnation(view_ds: &Dataset, expected: Option<&str>, what: &str) -> Result<()> {
+    let Some(expected) = expected else {
+        return Ok(());
+    };
+    let mut latest = view_ds.clone();
+    latest.checkout_latest().await?;
+    match latest.schema().metadata.get(INCARNATION_META_KEY) {
+        Some(actual) if actual == expected => Ok(()),
+        Some(_) => Err(Error::Runtime {
+            message: format!(
+                "materialized view '{what}' is not the incarnation this refresh was \
+                 requested for: it was dropped and recreated"
+            ),
+        }),
+        None => Err(Error::Runtime {
+            message: format!(
+                "materialized view '{what}' carries no incarnation token: its schema \
+                 metadata was replaced since the token was captured"
+            ),
+        }),
+    }
+}
+
 /// version this very commit produces. The version is predicted and then
 /// verified; on a mismatch another commit raced in between, and the stamp
 /// ABORTS rather than certify that commit as the refresh's own generation.
@@ -895,10 +981,21 @@ async fn stamp_watermark(
    mut dataset: Dataset,
    source_version: u64,
    source_ts: u128,
+    expected_incarnation: Option<&str>,
 ) -> Result<u64> {
+    ensure_incarnation(&dataset, expected_incarnation, dataset.uri()).await?;
    let predicted = dataset.version().version + 1;
+    // A view with no token (declared before tokens existed, or its metadata
+    // replaced wholesale) starts a new incarnation here.
+    let incarnation = dataset
+        .schema()
+        .metadata
+        .get(INCARNATION_META_KEY)
+        .cloned()
+        .unwrap_or_else(|| uuid::Uuid::new_v4().to_string());
    dataset
        .update_schema_metadata([
+            (INCARNATION_META_KEY.to_string(), Some(incarnation)),
            (
                SOURCE_VERSION_META_KEY.to_string(),
                Some(source_version.to_string()),
@@ -1052,12 +1149,14 @@ async fn publish(
    eviction: Option<(Vec<Fragment>, Vec<u64>)>,
    new_fragments: Vec<Fragment>,
    keys: Option<KeyExistenceFilter>,
+    expected_incarnation: Option<&str>,
 ) -> Result<Dataset> {
    let planned = view_ds.version().version;
    #[cfg(test)]
    tests::hold_before_publish(view_ds.uri()).await;
    #[cfg(test)]
    tests::hold_until_peers_planned();
+    ensure_incarnation(view_ds, expected_incarnation, view_ds.uri()).await?;
    let (updated_fragments, removed_fragment_ids) = eviction.unwrap_or_default();
    let committed = CommitBuilder::new(WriteDestination::Dataset(Arc::new(view_ds.clone())))
        .execute(Transaction::new(
@@ -1830,7 +1929,9 @@ mod tests {
        );
        let staged = eviction.finish().await.unwrap();
        assert!(staged.is_some(), "four ids over a chunk of two stage twice");
-        publish(&view_ds, staged, Vec::new(), None).await.unwrap();
+        publish(&view_ds, staged, Vec::new(), None, None)
+            .await
+            .unwrap();
        native.dataset.reload().await.unwrap();

        assert_eq!(read(view.table(), "x").await, vec![5, 6]);
@@ -2486,6 +2587,144 @@ mod tests {
        assert_eq!(read(view.table(), "twice").await, vec![14]);
    }

+    /// A refresh bound to an incarnation refuses a view dropped and recreated
+    /// since, even under the same name and definition; the recreated view's
+    /// own token is accepted, and the token survives a refresh's stamp.
+    #[tokio::test]
+    async fn test_refresh_refuses_a_recreated_view_incarnation() {
+        let (conn, _, view) = refreshed_doubled(vec![1]).await;
+        let token = view.incarnation().unwrap().to_string();
+        view.refresh()
+            .expect_incarnation(&token)
+            .execute()
+            .await
+            .unwrap();
+        let reopened = conn.open_materialized_view("doubled").await.unwrap();
+        assert_eq!(reopened.incarnation(), Some(token.as_str()));
+
+        conn.drop_table("doubled", &[]).await.unwrap();
+        let recreated = doubled_view(&conn).await;
+        assert_ne!(recreated.incarnation(), Some(token.as_str()));
+
+        let err = recreated
+            .refresh()
+            .expect_incarnation(&token)
+            .execute()
+            .await
+            .unwrap_err();
+        assert!(err.to_string().contains("dropped and recreated"), "{err}");
+        assert_eq!(read(recreated.table(), "twice").await, Vec::<i32>::new());
+
+        recreated
+            .refresh()
+            .expect_incarnation(recreated.incarnation().unwrap())
+            .execute()
+            .await
+            .unwrap();
+        assert_eq!(read(recreated.table(), "twice").await, vec![2]);
+    }
+
+    /// A cloned declaration creates two physical tables; each gets its own
+    /// token.
+    #[tokio::test]
+    async fn test_cloned_declaration_mints_a_fresh_incarnation_per_create() {
+        let (conn, source) = db_with_source(vec![1]).await;
+        let prepared = crate::materialized_view::prepare_declaration(
+            &source,
+            &[("x".into(), "x".into()), ("twice".into(), "x * 2".into())],
+            None,
+            None,
+        )
+        .await
+        .unwrap();
+        let replacement = prepared.clone();
+        let first = prepared.create("cloned").await.unwrap();
+        let first_token = first.incarnation().unwrap().to_string();
+
+        conn.drop_table("cloned", &[]).await.unwrap();
+        let second = replacement.create("cloned").await.unwrap();
+        assert_ne!(second.incarnation(), Some(first_token.as_str()));
+    }
+
+    /// A recreation that lands after planning but before publication is
+    /// caught by the pre-commit read: the stale refresh fails and the
+    /// replacement stays empty under its own token.
+    #[tokio::test(flavor = "multi_thread")]
+    async fn test_bound_refresh_cannot_publish_into_a_raced_recreation() {
+        let _serial = DRIFT_LOCK.lock().await;
+        let (conn, _) = db_with_source(vec![1]).await;
+        let view = doubled_view(&conn).await;
+        let token = view.incarnation().unwrap().to_string();
+        let uri = view
+            .table()
+            .as_native()
+            .unwrap()
+            .dataset
+            .get()
+            .await
+            .unwrap()
+            .uri()
+            .to_string();
+
+        *DRIFT_TARGET.lock().unwrap() = Some(uri);
+        let refreshing =
+            tokio::spawn(async move { view.refresh().expect_incarnation(token).execute().await });
+        tokio::time::timeout(std::time::Duration::from_secs(30), DRIFT_PLANNED.notified())
+            .await
+            .expect("refresh never reached publication");
+
+        conn.drop_table("doubled", &[]).await.unwrap();
+        let replacement = doubled_view(&conn).await;
+        let replacement_token = replacement.incarnation().unwrap().to_string();
+        DRIFT_RELEASED.notify_one();
+
+        let result = refreshing.await.unwrap();
+        assert!(result.is_err(), "the stale refresh unexpectedly succeeded");
+        let reopened = conn.open_materialized_view("doubled").await.unwrap();
+        assert_eq!(reopened.incarnation(), Some(replacement_token.as_str()));
+        assert_eq!(read(reopened.table(), "twice").await, Vec::<i32>::new());
+    }
+
+    /// Replacing the schema metadata wholesale drops the token. A refresh
+    /// bound to the old token is refused for that reason, not as a
+    /// recreation; an unbound refresh mints the view a fresh one.
+    #[tokio::test]
+    async fn test_a_view_whose_metadata_was_replaced_starts_a_new_incarnation() {
+        let (conn, _, view) = refreshed_doubled(vec![1]).await;
+        let token = view.incarnation().unwrap().to_string();
+        let mut metadata = HashMap::new();
+        metadata.insert(
+            crate::materialized_view::DEFINITION_META_KEY.to_string(),
+            crate::materialized_view::definition_to_metadata(view.definition()).unwrap(),
+        );
+        view.table()
+            .as_native()
+            .unwrap()
+            .replace_schema_metadata(metadata)
+            .await
+            .unwrap();
+        assert_eq!(
+            conn.open_materialized_view("doubled")
+                .await
+                .unwrap()
+                .incarnation(),
+            None
+        );
+
+        let err = view
+            .refresh()
+            .expect_incarnation(&token)
+            .execute()
+            .await
+            .unwrap_err();
+        assert!(err.to_string().contains("no incarnation token"), "{err}");
+
+        view.refresh().execute().await.unwrap();
+        let reopened = conn.open_materialized_view("doubled").await.unwrap();
+        assert!(reopened.incarnation().is_some());
+        assert_ne!(reopened.incarnation(), Some(token.as_str()));
+    }
+
    /// In-process refreshes of one view serialize: the loser of the race
    /// observes the winner's watermark instead of appending the same rows.
    #[tokio::test(flavor = "multi_thread")]
@@ -2528,7 +2767,7 @@ mod tests {
        let stale = view_native.dataset.get().await.unwrap().as_ref().clone();
        view.table().delete("x = 1").await.unwrap();

-        let err = stamp_watermark(view_native, stale, 99, 99).await;
+        let err = stamp_watermark(view_native, stale, 99, 99, None).await;
        assert!(err.is_err());

        let result = view.refresh().execute().await.unwrap();
@@ -1774,11 +1774,14 @@ mod tests {
            .postfilter();
        let result = query.execute().await;
        let mut stream = result.expect("should have result");
-        // should only have one batch
+        let mut num_rows = 0;
        while let Some(batch) = stream.next().await {
-            // post filter should have removed some rows
-            assert!(batch.expect("should be Ok").num_rows() < 10);
+            let batch = batch.expect("should be Ok");
+            let ids: &Int32Array = batch["id"].as_primitive();
+            assert!(ids.iter().all(|id| id.unwrap() % 2 == 0));
+            num_rows += batch.num_rows();
        }
+        assert!(num_rows <= 10);

        let query = table
            .query()
@@ -344,6 +344,62 @@ impl<S: HttpSend> RemoteDatabase<S> {
        self.table_cache.remove(&cache_key).await;
        Ok((request_id, resp))
    }
+
+    /// Collect the tables of a namespace in name order, for `table_names`.
+    ///
+    /// `table_names` promises name order and resumes after a table name, but the namespace
+    /// route's `page_token` is opaque -- it belongs to the store the listing walks, and a
+    /// token this client invented would resume from the wrong place. So the whole namespace is
+    /// walked by handing each response's token straight back, and the name semantics are
+    /// applied here. Constructing no token is what makes this work against a server on either
+    /// side of the change: it only ever repeats what the server said.
+    ///
+    /// This is the cost `table_names` already paid -- the server used to enumerate and sort the
+    /// namespace on every request -- and it is why `list_tables` replaces it.
+    async fn table_names_in_namespace(
+        &self,
+        request: &TableNamesRequest,
+    ) -> Result<(Vec<String>, ServerVersion)> {
+        let namespace_id =
+            build_namespace_identifier(&request.namespace_path, &self.client.id_delimiter);
+        let path = format!("/v1/namespace/{}/table/list", namespace_id);
+
+        let mut names = Vec::new();
+        // Every page reports the same server, so keep the first page's version.
+        let mut version: Option<ServerVersion> = None;
+        let mut page_token: Option<String> = None;
+        loop {
+            let mut req = self.client.get(&path);
+            if let Some(ref token) = page_token {
+                req = req.query(&[("page_token", token)]);
+            }
+            let (request_id, rsp) = self.client.send_with_retry(req, None, true).await?;
+            let rsp = self.client.check_response(&request_id, rsp).await?;
+            if version.is_none() {
+                version = Some(parse_server_version(&request_id, &rsp)?);
+            }
+            let response: ListTablesResponse = rsp.json().await.err_to_http(request_id)?;
+            names.extend(response.tables);
+            // An empty token is the end of the listing, not a token to send back: a server
+            // that reads an empty token as "start from the beginning" would hand back the
+            // first page again.
+            match response.page_token.filter(|token| !token.is_empty()) {
+                // A server that repeated a token would never finish; treat that as the end
+                // rather than looping on it.
+                Some(token) if Some(&token) != page_token.as_ref() => page_token = Some(token),
+                _ => break,
+            }
+        }
+
+        names.sort();
+        if let Some(ref start_after) = request.start_after {
+            names.retain(|name| name > start_after);
+        }
+        if let Some(limit) = request.limit {
+            names.truncate(limit as usize);
+        }
+        Ok((names, version.unwrap_or_default()))
+    }
 }

 #[cfg(all(test, feature = "remote"))]
@@ -621,29 +677,29 @@ impl<S: HttpSend> Database for RemoteDatabase<S> {
    }

    async fn table_names(&self, request: TableNamesRequest) -> Result<Vec<String>> {
-        let mut req = if !request.namespace_path.is_empty() {
-            let namespace_id =
-                build_namespace_identifier(&request.namespace_path, &self.client.id_delimiter);
-            self.client
-                .get(&format!("/v1/namespace/{}/table/list", namespace_id))
+        let (tables, version) = if request.namespace_path.is_empty() {
+            // The flat route resumes after a table name and orders by name, which is exactly
+            // what `start_after` means, so the server does the paging.
+            let mut req = self.client.get("/v1/table/");
+            if let Some(limit) = request.limit {
+                req = req.query(&[("limit", limit)]);
+            }
+            if let Some(ref start_after) = request.start_after {
+                req = req.query(&[("page_token", start_after)]);
+            }
+            let (request_id, rsp) = self.client.send_with_retry(req, None, true).await?;
+            let rsp = self.client.check_response(&request_id, rsp).await?;
+            let version = parse_server_version(&request_id, &rsp)?;
+            let tables = rsp
+                .json::<ListTablesResponse>()
+                .await
+                .err_to_http(request_id)?
+                .tables;
+            (tables, version)
        } else {
-            self.client.get("/v1/table/")
+            self.table_names_in_namespace(&request).await?
        };

-        if let Some(limit) = request.limit {
-            req = req.query(&[("limit", limit)]);
-        }
-        if let Some(start_after) = request.start_after {
-            req = req.query(&[("page_token", start_after)]);
-        }
-        let (request_id, rsp) = self.client.send_with_retry(req, None, true).await?;
-        let rsp = self.client.check_response(&request_id, rsp).await?;
-        let version = parse_server_version(&request_id, &rsp)?;
-        let tables = rsp
-            .json::<ListTablesResponse>()
-            .await
-            .err_to_http(request_id)?
-            .tables;
        for table in &tables {
            let table_identifier =
                build_table_identifier(table, &request.namespace_path, &self.client.id_delimiter);
@@ -1227,6 +1283,101 @@ mod tests {
        assert_eq!(names, vec!["table1", "table2"]);
    }

+    #[tokio::test]
+    async fn test_table_names_in_a_namespace_never_invents_a_page_token() {
+        // The namespace route's token belongs to the store, so `table_names` cannot build one
+        // from `start_after`. It walks the namespace on the server's own tokens and applies the
+        // name semantics itself, which is what keeps it working either side of the change.
+        let page = Arc::new(AtomicUsize::new(0));
+        let conn = Connection::new_with_handler(move |request| {
+            assert_eq!(request.url().path(), "/v1/namespace/ns/table/list");
+            let query = request.url().query().unwrap_or("");
+            assert!(
+                !query.contains("page_token=users"),
+                "a table name must never be sent as a page token: {query}"
+            );
+            match page.fetch_add(1, Ordering::SeqCst) {
+                0 => {
+                    assert!(
+                        !query.contains("page_token"),
+                        "the walk starts with no token"
+                    );
+                    http::Response::builder()
+                        .status(200)
+                        .body(r#"{"tables": ["users", "orders"], "page_token": "opaque-1"}"#)
+                        .unwrap()
+                }
+                _ => {
+                    assert!(query.contains("page_token=opaque-1"));
+                    http::Response::builder()
+                        .status(200)
+                        .body(r#"{"tables": ["widgets"]}"#)
+                        .unwrap()
+                }
+            }
+        });
+
+        let names = conn
+            .table_names()
+            .namespace(vec!["ns".to_string()])
+            .start_after("users")
+            .execute()
+            .await
+            .unwrap();
+        // Name order, resumed after "users": "orders" sorts before it and is dropped.
+        assert_eq!(names, vec!["widgets"]);
+    }
+
+    #[tokio::test]
+    async fn test_table_names_in_a_namespace_stops_on_a_repeated_token() {
+        // A server that handed back the token it was given would never finish the walk.
+        let conn = Connection::new_with_handler(|_request| {
+            http::Response::builder()
+                .status(200)
+                .body(r#"{"tables": ["a"], "page_token": "same"}"#)
+                .unwrap()
+        });
+
+        let names = conn
+            .table_names()
+            .namespace(vec!["ns".to_string()])
+            .execute()
+            .await
+            .unwrap();
+        // The guard bounds the walk instead of letting it run forever. The repeat is the
+        // server breaking the token contract and is not papered over here.
+        assert_eq!(names, vec!["a", "a"]);
+    }
+
+    #[tokio::test]
+    async fn test_table_names_in_a_namespace_stops_on_an_empty_token() {
+        // An empty token ends the listing. Sending it back would ask a server that reads it
+        // as "start from the beginning" for the first page a second time, and every name on
+        // that page would be collected twice.
+        let requests = Arc::new(AtomicUsize::new(0));
+        let seen = requests.clone();
+        let conn = Connection::new_with_handler(move |request| {
+            seen.fetch_add(1, Ordering::SeqCst);
+            assert!(
+                !request.url().query().unwrap_or("").contains("page_token"),
+                "an empty token must never be sent back"
+            );
+            http::Response::builder()
+                .status(200)
+                .body(r#"{"tables": ["a"], "page_token": ""}"#)
+                .unwrap()
+        });
+
+        let names = conn
+            .table_names()
+            .namespace(vec!["ns".to_string()])
+            .execute()
+            .await
+            .unwrap();
+        assert_eq!(names, vec!["a"]);
+        assert_eq!(requests.load(Ordering::SeqCst), 1);
+    }
+
    #[tokio::test]
    async fn test_table_names_pagination() {
        let conn = Connection::new_with_handler(|request| {
@@ -6,6 +6,7 @@
 use std::ops::Range;
 use std::sync::Arc;
 use std::sync::atomic::{AtomicBool, Ordering};
+use std::time::Duration;

 use arrow_array::{Array, LargeBinaryArray};
 use arrow_schema::DataType;
@@ -20,7 +21,7 @@ use crate::error::Result;
 use crate::remote::client::{HttpSend, RequestResultExt, RestfulLanceDbClient};
 use crate::table::BaseTable;

-use super::{FreshnessHeaders, RemoteTable};
+use super::{FreshnessHeaders, FreshnessState, RemoteTable, freshness_headers_snapshot};

 #[derive(Debug, Clone, Copy)]
 enum RangeRequestMode {
@@ -43,7 +44,10 @@ struct TableBlobRangeRequester<S: HttpSend> {
    path: String,
    version: Option<u64>,
    branch: Option<String>,
-    freshness: FreshnessHeaders,
+    freshness: Arc<std::sync::Mutex<FreshnessState>>,
+    parent_freshness: Arc<std::sync::Mutex<FreshnessState>>,
+    parent_freshness_request: FreshnessHeaders,
+    read_consistency_interval: Option<Duration>,
 }

 #[async_trait::async_trait]
@@ -53,8 +57,9 @@ impl<S: HttpSend> BlobRangeRequester for TableBlobRangeRequester<S> {
        range_header: &str,
        mode: RangeRequestMode,
    ) -> Result<(String, Response)> {
-        let mut request = self
-            .freshness
+        let freshness_request =
+            freshness_headers_snapshot(&self.freshness, self.read_consistency_interval);
+        let mut request = freshness_request
            .apply(self.client.get(&self.path))
            .header(header::RANGE, range_header);
        if let Some(version) = self.version {
@@ -71,6 +76,9 @@ impl<S: HttpSend> BlobRangeRequester for TableBlobRangeRequester<S> {
            return Ok((request_id, response));
        }
        let response = self.client.check_response(&request_id, response).await?;
+        freshness_request.observe_headers(&self.freshness, response.headers());
+        self.parent_freshness_request
+            .observe_headers(&self.parent_freshness, response.headers());
        Ok((request_id, response))
    }
 }
@@ -361,18 +369,21 @@ impl<S: HttpSend> RemoteTable<S> {
                message: "fetch_blobs is not supported on this LanceDB Cloud server".into(),
            });
        }
-        let version = self.current_version().await;
+        let read_snapshot = self.snapshot_read_state().await;
        let mut body = serde_json::json!({
-            "version": version,
+            "version": read_snapshot.version,
            "column": column,
            "row_ids": row_ids,
        });
        self.apply_branch_body(&mut body);

        let request = self
-            .post_read(&format!("/v1/table/{}/fetch_blobs/", self.identifier))
+            .client
+            .post(&format!("/v1/table/{}/fetch_blobs/", self.identifier))
            .json(&body);
-        let (request_id, response) = self.send(request, true).await?;
+        let (request_id, response) = self
+            .send_with_freshness(request, true, read_snapshot.freshness)
+            .await?;
        let mut stream = self.read_arrow_response(&request_id, response).await?;

        let mut blob_chunks: Vec<Arc<dyn Array>> = Vec::new();
@@ -448,8 +459,7 @@ impl<S: HttpSend> RemoteTable<S> {
            });
        }

-        let version = self.current_version().await;
-        let freshness = self.snapshot_freshness_headers();
+        let read_snapshot = self.snapshot_read_state().await;
        let encoded_column = urlencoding::encode(column);
        let requesters = row_ids
            .iter()
@@ -461,9 +471,12 @@ impl<S: HttpSend> RemoteTable<S> {
                let requester: Arc<dyn BlobRangeRequester> = Arc::new(TableBlobRangeRequester {
                    client: self.client.clone(),
                    path,
-                    version,
+                    version: read_snapshot.version,
                    branch: self.branch.clone(),
-                    freshness,
+                    freshness: Arc::new(std::sync::Mutex::new(read_snapshot.freshness_state)),
+                    parent_freshness: self.freshness.clone(),
+                    parent_freshness_request: read_snapshot.freshness,
+                    read_consistency_interval: self.client.read_consistency_interval,
                });
                requester
            })
@@ -685,6 +698,46 @@ mod tests {
        assert!(requests.lock().unwrap().contains(&"bytes=5-11".to_string()));
    }

+    #[tokio::test]
+    async fn remote_blob_file_keeps_the_open_timeline_after_parent_checkout() {
+        let range_requests = Arc::new(StdMutex::new(Vec::new()));
+        let captured = range_requests.clone();
+        let table = RemoteTable::new_mock(
+            "my_table".to_string(),
+            move |request| match request.url().path() {
+                "/v1/table/my_table/describe/" => http::Response::builder()
+                    .status(200)
+                    .body(r#"{"version":5,"schema":{"fields":[]}}"#.as_bytes().to_vec())
+                    .unwrap(),
+                "/v1/table/my_table/blob/image/10/bytes" => {
+                    captured.lock().unwrap().push((
+                        request.url().query().unwrap_or_default().to_string(),
+                        request.headers().clone(),
+                    ));
+                    range_response(&request, PAYLOAD)
+                }
+                path => panic!("unexpected path: {path}"),
+            },
+            Some(Version::new(0, 5, 0)),
+        );
+
+        table.checkout(5).await.unwrap();
+        let file = table
+            .fetch_blob_files_impl("image", &[10])
+            .await
+            .unwrap()
+            .pop()
+            .flatten()
+            .unwrap();
+        table.checkout_latest().await.unwrap();
+        file.read_range(5..12).await.unwrap();
+
+        let requests = range_requests.lock().unwrap();
+        let (query, headers) = requests.last().unwrap();
+        assert!(query.contains("version=5"));
+        assert!(!headers.contains_key("x-lancedb-min-timestamp"));
+    }
+
    #[tokio::test]
    async fn remote_blob_file_reuses_sequential_response_until_seek() {
        let requests = Arc::new(StdMutex::new(Vec::new()));
@@ -24,7 +24,10 @@ use lance::io::exec::utils::InstrumentedRecordBatchStreamAdapter;
 use crate::Error;
 use crate::remote::ARROW_STREAM_CONTENT_TYPE;
 use crate::remote::client::{HttpSend, RestfulLanceDbClient, Sender};
-use crate::remote::table::{MergeInsertRequest, REQUEST_TIMEOUT_HEADER, RemoteTable};
+use crate::remote::table::{
+    FreshnessHeaders, FreshnessState, MergeInsertRequest, REQUEST_TIMEOUT_HEADER, RemoteTable,
+    freshness_headers_snapshot,
+};
 use crate::table::datafusion::insert::COUNT_SCHEMA;
 use crate::table::write_progress::WriteProgressTracker;
 use crate::table::{AddResult, MergeResult};
@@ -54,6 +57,38 @@ pub enum WriteResult {
    Merge(MergeResult),
 }

+#[derive(Debug, Clone, Default)]
+struct WriteFreshness {
+    state: Option<Arc<Mutex<FreshnessState>>>,
+    read_consistency_interval: Option<Duration>,
+}
+
+impl WriteFreshness {
+    fn prepare(
+        &self,
+        request: reqwest::RequestBuilder,
+    ) -> (reqwest::RequestBuilder, Option<FreshnessHeaders>) {
+        match &self.state {
+            Some(state) => {
+                let freshness_request =
+                    freshness_headers_snapshot(state, self.read_consistency_interval);
+                (freshness_request.apply(request), Some(freshness_request))
+            }
+            None => (request, None),
+        }
+    }
+
+    fn observe(
+        &self,
+        freshness_request: Option<FreshnessHeaders>,
+        headers: &reqwest::header::HeaderMap,
+    ) {
+        if let (Some(state), Some(freshness_request)) = (&self.state, freshness_request) {
+            freshness_request.observe_headers(state, headers);
+        }
+    }
+}
+
 /// ExecutionPlan for streaming a write (add or merge_insert) to a remote
 /// LanceDB table.
 ///
@@ -71,6 +106,7 @@ pub struct RemoteWriteExec<S: HttpSend = Sender> {
    table_name: String,
    identifier: String,
    client: RestfulLanceDbClient<S>,
+    freshness: WriteFreshness,
    input: Arc<dyn ExecutionPlan>,
    op: WriteOp,
    properties: Arc<PlanProperties>,
@@ -170,6 +206,7 @@ impl<S: HttpSend + 'static> RemoteWriteExec<S> {
            table_name,
            identifier,
            client,
+            freshness: WriteFreshness::default(),
            input,
            op,
            properties: Arc::new(properties),
@@ -183,6 +220,18 @@ impl<S: HttpSend + 'static> RemoteWriteExec<S> {
        }
    }

+    pub(super) fn with_freshness(
+        mut self,
+        state: Arc<Mutex<FreshnessState>>,
+        read_consistency_interval: Option<Duration>,
+    ) -> Self {
+        self.freshness = WriteFreshness {
+            state: Some(state),
+            read_consistency_interval,
+        };
+        self
+    }
+
    /// Get the add result after execution, if this exec ran an insert.
    pub fn add_result(&self) -> Option<AddResult> {
        match self
@@ -285,6 +334,7 @@ impl<S: HttpSend + 'static> RemoteWriteExec<S> {
 /// each threading the same handful of arguments.
 struct PartRequestCtx<'a, S: HttpSend> {
    client: &'a RestfulLanceDbClient<S>,
+    freshness: &'a WriteFreshness,
    identifier: &'a str,
    table_name: &'a str,
    upload_id: &'a str,
@@ -352,7 +402,11 @@ impl<S: HttpSend + 'static> PartRequestCtx<'_, S> {
    }

    /// Build the `/insert` request for a single multipart part.
-    fn build_part_request(&self, part_id: &str, body: reqwest::Body) -> reqwest::RequestBuilder {
+    fn build_part_request(
+        &self,
+        part_id: &str,
+        body: reqwest::Body,
+    ) -> (reqwest::RequestBuilder, Option<FreshnessHeaders>) {
        let mut request = self
            .client
            .post(&format!("/v1/table/{}/insert/", self.identifier))
@@ -368,12 +422,16 @@ impl<S: HttpSend + 'static> PartRequestCtx<'_, S> {
        if let Some(b) = self.branch {
            request = request.query(&[("branch", b)]);
        }
-        request.body(body)
+        self.freshness.prepare(request.body(body))
    }

    /// Send a single part's request and drain the response, mapping HTTP and
    /// table-not-found errors into `DataFusionError`.
-    async fn send_part_request(&self, request: reqwest::RequestBuilder) -> DataFusionResult<()> {
+    async fn send_part_request(
+        &self,
+        request: reqwest::RequestBuilder,
+        freshness_request: Option<FreshnessHeaders>,
+    ) -> DataFusionResult<()> {
        let (request_id, response) = self
            .client
            .send(request)
@@ -388,6 +446,8 @@ impl<S: HttpSend + 'static> PartRequestCtx<'_, S> {
            .check_response(&request_id, response)
            .await
            .map_err(|e| DataFusionError::External(Box::new(e)))?;
+        self.freshness
+            .observe(freshness_request, response.headers());
        response.bytes().await.map_err(|e| {
            DataFusionError::External(Box::new(Error::Http {
                source: Box::new(e),
@@ -419,7 +479,7 @@ impl<S: HttpSend + 'static> PartRequestCtx<'_, S> {
        let body = reqwest::Body::wrap_stream(chunk_rx);

        let part_id = uuid::Uuid::new_v4().to_string();
-        let request = self.build_part_request(&part_id, body);
+        let (request, freshness_request) = self.build_part_request(&part_id, body);

        // Measured from just before the request is sent, matching the window the
        // client read timeout applies to the upload.
@@ -495,7 +555,7 @@ impl<S: HttpSend + 'static> PartRequestCtx<'_, S> {
            Ok::<bool, DataFusionError>(input_ended)
        };

-        let send = self.send_part_request(request);
+        let send = self.send_part_request(request, freshness_request);

        // `join!` rather than `tokio::spawn`: the producer borrows `input` (and
        // `schema`), so it cannot satisfy the `'static` bound a spawned task
@@ -569,7 +629,7 @@ impl<S: HttpSend + 'static> ExecutionPlan for RemoteWriteExec<S> {
        // Building a fresh exec (with a new, empty `result`) is what makes the
        // outer rescannable retry loop work: `reset_state()` clears the captured
        // result so a re-execution starts clean.
-        Ok(Arc::new(Self::new_inner(
+        let mut exec = Self::new_inner(
            self.table_name.clone(),
            self.identifier.clone(),
            self.client.clone(),
@@ -580,7 +640,9 @@ impl<S: HttpSend + 'static> ExecutionPlan for RemoteWriteExec<S> {
            self.branch.clone(),
            self.max_bytes_per_request,
            self.max_request_duration,
-        )))
+        );
+        exec.freshness = self.freshness.clone();
+        Ok(Arc::new(exec))
    }

    fn execute(
@@ -613,6 +675,7 @@ impl<S: HttpSend + 'static> ExecutionPlan for RemoteWriteExec<S> {
                &self.metrics,
            ));
        let client = self.client.clone();
+        let freshness = self.freshness.clone();
        let identifier = self.identifier.clone();
        let op = self.op.clone();
        let result_slot = self.result.clone();
@@ -634,6 +697,7 @@ impl<S: HttpSend + 'static> ExecutionPlan for RemoteWriteExec<S> {
                let overwrite = matches!(op, WriteOp::Insert { overwrite: true });
                let ctx = PartRequestCtx {
                    client: &client,
+                    freshness: &freshness,
                    identifier: &identifier,
                    table_name: &table_name,
                    upload_id,
@@ -688,7 +752,7 @@ impl<S: HttpSend + 'static> ExecutionPlan for RemoteWriteExec<S> {

            let (error_tx, mut error_rx) = tokio::sync::oneshot::channel();
            let body = Self::stream_as_http_body(input_stream, error_tx, tracker)?;
-            let request = request.body(body);
+            let (request, freshness_request) = freshness.prepare(request.body(body));

            let result: DataFusionResult<(String, _)> = async {
                let (request_id, response) = client
@@ -708,6 +772,7 @@ impl<S: HttpSend + 'static> ExecutionPlan for RemoteWriteExec<S> {
                    .check_response(&request_id, response)
                    .await
                    .map_err(|e| DataFusionError::External(Box::new(e)))?;
+                freshness.observe(freshness_request, response.headers());

                Ok((request_id, response))
            }
@@ -560,6 +560,13 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
    fn id(&self) -> &str;
    /// Get the arrow [Schema] of the table.
    async fn schema(&self) -> Result<SchemaRef>;
+    /// Create a read-only handle pinned to the table's current active revision.
+    ///
+    /// The returned handle is independent from later refreshes or checkouts on
+    /// this handle.  This is used by bindings that must prepare client-side
+    /// query state from the same revision that the query will execute against.
+    #[doc(hidden)]
+    async fn query_snapshot(&self) -> Result<Arc<dyn BaseTable>>;
    /// Count the number of rows in this table.
    async fn count_rows(&self, filter: Option<Filter>) -> Result<usize>;
    /// Create a physical plan for the query.
@@ -751,7 +758,7 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
            message: "computed columns are not supported on this table type".into(),
        })
    }
-    /// Declare one immutable registered-Function output group.
+    /// Declare one immutable registered-Function binding.
    async fn add_function_columns(
        &self,
        _application: &crate::function::FunctionApplication,
@@ -785,6 +792,12 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
    async fn drop_columns(&self, columns: &[&str]) -> Result<DropColumnsResult>;
    /// Get the version of the table.
    async fn version(&self) -> Result<u64>;
+    /// Return a new table handle pinned to the exact revision currently visible.
+    async fn checkout_current(&self) -> Result<Arc<dyn BaseTable>> {
+        Err(Error::NotSupported {
+            message: "checkout_current is not supported on this table type".into(),
+        })
+    }
    /// Checkout a specific version of the table.
    async fn checkout(&self, version: u64) -> Result<()>;
    /// Checkout a table version referenced by a tag.
@@ -792,6 +805,14 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
    async fn checkout_tag(&self, tag: &str) -> Result<()>;
    /// Checkout the latest version of the table.
    async fn checkout_latest(&self) -> Result<()>;
+    /// Return an independent handle pinned to the version currently selected.
+    ///
+    /// Backends that can advance between requests should override this for
+    /// multi-request operations that need snapshot consistency. Backends whose
+    /// existing handles already provide the desired behavior return `None`.
+    async fn snapshot_at_current_version(&self) -> Result<Option<Arc<dyn BaseTable>>> {
+        Ok(None)
+    }
    /// Whether repeated identical scans return rows in the same order.
    ///
    /// Callers that assign meaning to a row's position must order the results
@@ -1125,6 +1146,16 @@ impl Table {
        self.inner.schema().await
    }

+    /// Create a read-only handle pinned to the current active revision.
+    #[doc(hidden)]
+    pub async fn query_snapshot(&self) -> Result<Self> {
+        Ok(Self {
+            inner: self.inner.query_snapshot().await?,
+            database: self.database.clone(),
+            embedding_registry: self.embedding_registry.clone(),
+        })
+    }
+
    /// Count the number of rows in this dataset.
    ///
    /// # Arguments
@@ -1936,6 +1967,20 @@ impl Table {
        self.inner.version().await
    }

+    /// Return a new table handle pinned to the exact revision currently visible.
+    ///
+    /// This is used when asynchronous preparation must remain consistent with
+    /// the revision used for a later read.
+    #[doc(hidden)]
+    pub async fn checkout_current(&self) -> Result<Self> {
+        let inner = self.inner.checkout_current().await?;
+        Ok(Self {
+            inner,
+            database: self.database.clone(),
+            embedding_registry: self.embedding_registry.clone(),
+        })
+    }
+
    /// Checks out a specific version of the Table
    ///
    /// Any read operation on the table will now access the data at the checked out version.
@@ -3031,10 +3076,33 @@ impl BaseTable for NativeTable {
        &self.id
    }

+    async fn query_snapshot(&self) -> Result<Arc<dyn BaseTable>> {
+        let snapshot = self.dataset.new_query_snapshot().await?;
+        let mut table = self.with_dataset(snapshot);
+        // QueryTable requests do not carry a revision. A pinned snapshot must
+        // execute locally until the namespace API can accept that revision.
+        table
+            .pushdown_operations
+            .remove(&NamespaceClientPushdownOperation::QueryTable);
+        Ok(Arc::new(table))
+    }
+
    async fn version(&self) -> Result<u64> {
        Ok(self.dataset.get().await?.version().version)
    }

+    async fn checkout_current(&self) -> Result<Arc<dyn BaseTable>> {
+        let current = self.dataset.get().await?;
+        let dataset = dataset::DatasetConsistencyWrapper::new_time_travel(
+            current.as_ref().clone(),
+            self.read_consistency_interval,
+        );
+        Ok(Arc::new(Self {
+            dataset,
+            ..self.clone()
+        }))
+    }
+
    async fn checkout(&self, version: u64) -> Result<()> {
        self.dataset.as_time_travel(version).await
    }
@@ -3200,7 +3268,7 @@ impl BaseTable for NativeTable {

        let output = add.into_plan(&table_schema, &table_def)?;

-        let lance_params = output
+        let mut lance_params = output
            .write_options
            .lance_write_params
            .unwrap_or(WriteParams {
@@ -3210,6 +3278,9 @@ impl BaseTable for NativeTable {
                },
                ..Default::default()
            });
+        if output.allow_external_blob_outside_bases {
+            lance_params.allow_external_blob_outside_bases = true;
+        }

        // Repartition for write parallelism if beneficial.
        let plan = if num_partitions > 1 {
@@ -4075,6 +4146,14 @@ mod tests {
                parent_list_calls: self.parent_list_calls.clone(),
            })
        }
+
+        fn wrap_paginated(
+            &self,
+            _store_prefix: &str,
+            _original: Arc<dyn object_store::list::PaginatedListStore>,
+        ) -> Option<Arc<dyn object_store::list::PaginatedListStore>> {
+            None
+        }
    }

    #[tokio::test]
@@ -4178,6 +4257,14 @@ mod tests {
            self.called.store(true, Ordering::Relaxed);
            original
        }
+
+        fn wrap_paginated(
+            &self,
+            _store_prefix: &str,
+            original: Arc<dyn object_store::list::PaginatedListStore>,
+        ) -> Option<Arc<dyn object_store::list::PaginatedListStore>> {
+            Some(original)
+        }
    }

    #[tokio::test]
@@ -88,7 +88,7 @@ impl AddColumnsBuilder {
    }

    /// Declare every field of a named-struct Function result as one atomic
-    /// sibling group. Result-field aliases come from
+    /// binding. Result-field aliases come from
    /// [`FunctionApplication::columns`](crate::function::FunctionApplication::columns).
    ///
    /// ```
@@ -60,6 +60,7 @@ pub struct AddDataBuilder {
    pub(crate) embedding_registry: Option<Arc<dyn EmbeddingRegistry>>,
    pub(crate) progress_callback: Option<ProgressCallback>,
    pub(crate) write_parallelism: Option<usize>,
+    pub(crate) allow_external_blob_outside_bases: bool,
 }

 impl std::fmt::Debug for AddDataBuilder {
@@ -87,6 +88,7 @@ impl AddDataBuilder {
            embedding_registry,
            progress_callback: None,
            write_parallelism: None,
+            allow_external_blob_outside_bases: false,
        }
    }

@@ -141,6 +143,16 @@ impl AddDataBuilder {
        self
    }

+    /// Store blob URIs that sit outside registered blob bases.
+    ///
+    /// The row keeps a reference, so the object has to stay readable.
+    /// [`crate::table::Table::fetch_blobs`] reads from that location.
+    /// Defaults to `false`. Local tables only.
+    pub fn allow_external_blob_outside_bases(mut self, allow: bool) -> Self {
+        self.allow_external_blob_outside_bases = allow;
+        self
+    }
+
    pub async fn execute(self) -> Result<AddResult> {
        if self.write_parallelism.map(|p| p == 0).unwrap_or(false) {
            return Err(Error::InvalidInput {
@@ -199,6 +211,7 @@ impl AddDataBuilder {
            write_options: self.write_options,
            mode: self.mode,
            tracker,
+            allow_external_blob_outside_bases: self.allow_external_blob_outside_bases,
        })
    }
 }
@@ -212,6 +225,7 @@ pub struct PreprocessingOutput {
    pub write_options: WriteOptions,
    pub mode: AddDataMode,
    pub tracker: Option<Arc<WriteProgressTracker>>,
+    pub allow_external_blob_outside_bases: bool,
 }

 /// Check that the input schema is valid for insert.
@@ -13,7 +13,7 @@
 //! self-describing -- both are derived from the expression, so a caller writes
 //! neither -- while a kind resolved through a registry cannot be typed without
 //! consulting it. Registered Functions use an exact remote version plus a
-//! schema-level grouped binding; unknown newer kinds remain readable and fail
+//! schema-level Function binding; unknown newer kinds remain readable and fail
 //! closed before mutation.
 //!
 //! [`computed_columns`] and [`computed_column_from_field`] read declarations
@@ -46,16 +46,16 @@ pub const EXPRESSION_META_KEY: &str = "computed_column.expression";
 /// Field metadata key holding the column's inputs, as a JSON array of names.
 pub const INPUTS_META_KEY: &str = "computed_column.inputs";

-/// Field metadata key holding the grouped Function binding identity.
+/// Field metadata key holding the Function binding identity.
 pub const FUNCTION_BINDING_ID_META_KEY: &str = "computed_column.function.binding_id";

 /// Field metadata key holding this sibling's ordered Function output ordinal.
 pub const FUNCTION_OUTPUT_ORDINAL_META_KEY: &str = "computed_column.function.output_ordinal";

-/// Schema metadata key holding all immutable grouped Function bindings.
+/// Schema metadata key holding all immutable Function bindings.
 pub const FUNCTION_BINDINGS_META_KEY: &str = "lancedb::function_bindings";

-/// Version of the schema-level grouped Function binding envelope.
+/// Version of the schema-level Function binding envelope.
 pub const FUNCTION_BINDINGS_VERSION: u32 = 1;

 /// Value of [`KIND_META_KEY`] for a column defined by a SQL expression.
@@ -81,7 +81,7 @@ pub enum ComputedColumnKind {
        /// The expression.
        expression: String,
    },
-    /// One physical output in an immutable grouped registered-Function
+    /// One physical output in an immutable registered-Function
    /// binding. The full binding lives in schema metadata.
    Function {
        /// Shared immutable binding identity.
@@ -159,7 +159,7 @@ struct FunctionBindingEnvelope {
    bindings: Vec<Value>,
 }

-/// Encode immutable grouped bindings for schema-level persistence.
+/// Encode immutable Function bindings for schema-level persistence.
 pub fn function_bindings_metadata(bindings: &[FunctionBinding]) -> Result<String> {
    let bindings = bindings
        .iter()
@@ -177,7 +177,7 @@ pub fn function_bindings_metadata(bindings: &[FunctionBinding]) -> Result<String
    })
 }

-/// Decode known grouped Function bindings without rewriting their raw schema
+/// Decode known Function bindings without rewriting their raw schema
 /// metadata. Unknown envelope versions fail closed.
 pub fn function_bindings(schema: &ArrowSchema) -> Result<Vec<FunctionBinding>> {
    let Some(envelope) = function_binding_envelope(schema)? else {
@@ -238,21 +238,15 @@ pub(crate) fn ensure_supported_function_metadata(schema: &ArrowSchema) -> Result
                message: format!("duplicate Function binding '{}'", binding.binding_id()),
            });
        }
-        if binding.revision() == 0 || binding.outputs().is_empty() {
+        if binding.outputs().is_empty() {
            return Err(Error::InvalidInput {
-                message: format!(
-                    "Function binding '{}' has no immutable revision or outputs",
-                    binding.binding_id()
-                ),
+                message: format!("Function binding '{}' has no outputs", binding.binding_id()),
            });
        }
-        if binding.function().name.is_empty()
-            || binding.function().version.is_empty()
-            || binding.group_id().is_empty()
-        {
+        if binding.function().name.is_empty() || binding.function().version.is_empty() {
            return Err(Error::InvalidInput {
                message: format!(
-                    "Function binding '{}' has no exact version or group identity",
+                    "Function binding '{}' has no exact version",
                    binding.binding_id()
                ),
            });
@@ -493,9 +487,7 @@ fn ensure_known_binding_shape(value: &Value) -> Result<()> {
        value,
        &[
            "binding_id",
-            "revision",
            "function",
-            "group_id",
            "inputs",
            "outputs",
            "input_schema",
@@ -586,6 +578,25 @@ fn canonical_input_arrow_type(field: &JsonArrowField) -> Result<String> {
    }
 }

+/// `fixed_size_list<item, size>` -> (`item`, `size`); the comma must sit outside
+/// any nested `<...>`.
+fn split_fixed_size_list(raw: &str) -> Option<(&str, i32)> {
+    let inner = raw.strip_prefix("fixed_size_list<")?.strip_suffix('>')?;
+    let mut depth = 0_u32;
+    let mut separator = None;
+    for (index, byte) in inner.bytes().enumerate() {
+        match byte {
+            b'<' => depth += 1,
+            b'>' => depth = depth.checked_sub(1)?,
+            b',' if depth == 0 => separator = Some(index),
+            _ => {}
+        }
+    }
+    let (item, size) = inner.split_at(separator?);
+    let size: i32 = size[1..].trim().parse().ok()?;
+    (size > 0).then_some((item.trim(), size))
+}
+
 fn parse_output_arrow_type(raw: &str) -> Result<JsonArrowDataType> {
    fn parse(raw: &str) -> Result<JsonArrowDataType> {
        let raw = raw.trim();
@@ -618,6 +629,16 @@ fn parse_output_arrow_type(raw: &str) -> Result<JsonArrowDataType> {
            )]);
            return Ok(data_type);
        }
+        if let Some((inner, size)) = split_fixed_size_list(raw) {
+            let mut data_type = JsonArrowDataType::new("fixed_size_list".to_string());
+            data_type.fields = Some(vec![JsonArrowField::new(
+                "item".to_string(),
+                false,
+                parse(inner)?,
+            )]);
+            data_type.length = Some(i64::from(size));
+            return Ok(data_type);
+        }
        let normalized = match raw {
            "boolean" => "bool",
            "string" => "utf8",
@@ -757,12 +778,9 @@ pub(crate) fn plan_function_application(
            message: "Function application contains fields from a newer contract".into(),
        });
    }
-    if application.function().name.is_empty()
-        || application.function().version.is_empty()
-        || application.group_id().is_empty()
-    {
+    if application.function().name.is_empty() || application.function().version.is_empty() {
        return Err(invalid_function(
-            "Function application requires an exact version and group identity",
+            "Function application requires an exact version",
        ));
    }

@@ -1322,6 +1340,32 @@ pub(super) async fn add_foreign_kind(table: &crate::Table, name: &str, kind: &st

 #[cfg(test)]
 mod tests {
+    #[test]
+    fn output_arrow_type_grammar_matches_the_shared_golden() {
+        let golden: serde_json::Value = serde_json::from_str(include_str!(
+            "../../tests/fixtures/first_class_functions/v1/arrow_types.json"
+        ))
+        .unwrap();
+        let valid = golden["valid"].as_array().unwrap().iter();
+        for case in valid.chain(golden["server_only"].as_array().unwrap()) {
+            let raw = case["arrow_type"].as_str().unwrap();
+            let parsed = super::parse_output_arrow_type(raw)
+                .unwrap_or_else(|error| panic!("{raw}: {error}"));
+            assert_eq!(
+                serde_json::to_value(&parsed).unwrap(),
+                case["json"],
+                "{raw}"
+            );
+        }
+        for raw in golden["invalid"].as_array().unwrap() {
+            let raw = raw.as_str().unwrap();
+            assert!(
+                super::parse_output_arrow_type(raw).is_err(),
+                "{raw:?} should be rejected"
+            );
+        }
+    }
+
    use arrow_array::record_batch;
    use arrow_schema::DataType;
    use futures::TryStreamExt;
@@ -2206,7 +2250,6 @@ mod tests {
                    {{"name":"normalized_text","arrow_type":"utf8","nullable":false}},
                    {{"name":"token_count","arrow_type":"int64","nullable":false}}
                ]}},
-                "group_id":"fg_exact",
                "columns":{columns}
            }}"#
        ))
@@ -2387,8 +2430,7 @@ mod tests {
            r#"{
                "function":{"name":"f","version":"fv"},
                "inputs":[{"parameter":"title","kind":"future_source","value":{"path":"title"}}],
-                "output":{"kind":"scalar","arrow_type":"int64","nullable":false},
-                "group_id":"fg"
+                "output":{"kind":"scalar","arrow_type":"int64","nullable":false}
            }"#,
        )
        .unwrap();
@@ -2401,7 +2443,6 @@ mod tests {
                "function":{"name":"f","version":"fv"},
                "inputs":[],
                "output":{"kind":"scalar","arrow_type":"int64","nullable":false},
-                "group_id":"fg",
                "future_declaration":{"mode":"managed"}
            }"#,
        )
@@ -2415,8 +2456,7 @@ mod tests {
            r#"{
                "function":{"name":"f","version":"fv"},
                "inputs":[],
-                "output":{"kind":"scalar","arrow_type":"int64","nullable":false,"assignment":"cell_flag"},
-                "group_id":"fg"
+                "output":{"kind":"scalar","arrow_type":"int64","nullable":false,"assignment":"cell_flag"}
            }"#,
        )
        .unwrap();
@@ -7,7 +7,7 @@

 use std::sync::Arc;

-use arrow_schema::{DataType, Field, FieldRef};
+use arrow_schema::{DataType, Field, FieldRef, Fields};
 use datafusion::functions::core::{get_field, named_struct};
 use datafusion_common::ScalarValue;
 use datafusion_common::config::ConfigOptions;
@@ -35,8 +35,9 @@ pub(super) fn coerce_blob_expr(
        });
    };

-    let input_struct_children = match input_field.data_type() {
-        DataType::Binary | DataType::LargeBinary | DataType::BinaryView => None,
+    let input_shape = match input_field.data_type() {
+        DataType::Binary | DataType::LargeBinary | DataType::BinaryView => BlobInputShape::Bytes,
+        DataType::Utf8 | DataType::LargeUtf8 | DataType::Utf8View => BlobInputShape::String,
        DataType::Struct(children) => {
            if !children
                .iter()
@@ -49,13 +50,15 @@ pub(super) fn coerce_blob_expr(
                    ),
                });
            }
-            Some(children)
+            BlobInputShape::Struct(children)
        }
        other => {
            return Err(Error::InvalidInput {
                message: format!(
                    "cannot coerce column '{}' with type {} into a blob v2 struct. \
-                     expected Binary, LargeBinary, BinaryView, or a Struct with a 'data' or 'uri' child",
+                     expected binary bytes (Binary, LargeBinary, BinaryView), \
+                     strings (Utf8, LargeUtf8, Utf8View), \
+                     or a Struct with a 'data' or 'uri' child",
                    table_field.name(),
                    other,
                ),
@@ -69,9 +72,8 @@ pub(super) fn coerce_blob_expr(
            declared.name().as_str(),
        ))));

-        let value: Arc<dyn PhysicalExpr> = match input_struct_children {
-            // Raw binary lands in `data` and everything else is a typed null.
-            None => {
+        let value: Arc<dyn PhysicalExpr> = match &input_shape {
+            BlobInputShape::Bytes => {
                if declared.name() == "data" {
                    Arc::new(CastExpr::new(
                        input_expr.clone(),
@@ -82,30 +84,43 @@ pub(super) fn coerce_blob_expr(
                    typed_null(declared.data_type())?
                }
            }
-            Some(children) => match children.iter().find(|c| c.name() == declared.name()) {
-                Some(child) => {
-                    let field_expr: Arc<dyn PhysicalExpr> = Arc::new(ScalarFunctionExpr::new(
-                        &format!("get_field({})", declared.name()),
-                        get_field(),
-                        vec![
-                            input_expr.clone(),
-                            Arc::new(Literal::new(ScalarValue::from(declared.name().as_str()))),
-                        ],
-                        Arc::new(child.as_ref().clone()),
-                        config.clone(),
-                    ));
-                    if child.data_type() == declared.data_type() {
-                        field_expr
-                    } else {
-                        Arc::new(CastExpr::new(
-                            field_expr,
-                            declared.data_type().clone(),
-                            None,
-                        ))
-                    }
+            BlobInputShape::String => {
+                if declared.name() == "uri" {
+                    Arc::new(CastExpr::new(
+                        input_expr.clone(),
+                        declared.data_type().clone(),
+                        None,
+                    ))
+                } else {
+                    typed_null(declared.data_type())?
                }
-                None => typed_null(declared.data_type())?,
-            },
+            }
+            BlobInputShape::Struct(children) => {
+                match children.iter().find(|c| c.name() == declared.name()) {
+                    Some(child) => {
+                        let field_expr: Arc<dyn PhysicalExpr> = Arc::new(ScalarFunctionExpr::new(
+                            &format!("get_field({})", declared.name()),
+                            get_field(),
+                            vec![
+                                input_expr.clone(),
+                                Arc::new(Literal::new(ScalarValue::from(declared.name().as_str()))),
+                            ],
+                            Arc::new(child.as_ref().clone()),
+                            config.clone(),
+                        ));
+                        if child.data_type() == declared.data_type() {
+                            field_expr
+                        } else {
+                            Arc::new(CastExpr::new(
+                                field_expr,
+                                declared.data_type().clone(),
+                                None,
+                            ))
+                        }
+                    }
+                    None => typed_null(declared.data_type())?,
+                }
+            }
        };
        ns_args.push(value);
    }
@@ -120,6 +135,12 @@ pub(super) fn coerce_blob_expr(
    Ok((expr, table_field.clone()))
 }

+enum BlobInputShape<'a> {
+    Bytes,
+    String,
+    Struct(&'a Fields),
+}
+
 fn typed_null(data_type: &DataType) -> Result<Arc<dyn PhysicalExpr>> {
    let scalar = ScalarValue::try_from(data_type).map_err(|e| Error::InvalidInput {
        message: format!("cannot build null literal for blob child type {data_type}: {e}"),
@@ -134,7 +155,7 @@ mod tests {
    use crate::blob::blob;
    use arrow_array::{
        Array, ArrayRef, BinaryArray, BinaryViewArray, Int32Array, Int64Array, LargeBinaryArray,
-        RecordBatch, StringArray, StructArray, UInt8Array, UInt64Array,
+        RecordBatch, StringArray, StringViewArray, StructArray, UInt8Array, UInt64Array,
    };
    use arrow_schema::Schema;
    use datafusion::prelude::SessionContext;
@@ -436,14 +457,78 @@ mod tests {
    #[tokio::test]
    async fn unsupported_input_type_is_rejected_with_column_name() {
        let batch = batch_with_image(
-            Field::new("image", DataType::Utf8, true),
-            Arc::new(StringArray::from(vec!["not bytes"])),
+            Field::new("image", DataType::Int64, true),
+            Arc::new(Int64Array::from(vec![42])),
        );
        let err = coerce_err(batch, &blob_table_schema()).await;
        assert!(matches!(err, Error::InvalidInput { .. }), "got {err:?}");
        assert!(err.to_string().contains("image"));
    }

+    #[tokio::test]
+    async fn utf8_string_coerces_to_uri_child() {
+        let batch = batch_with_image(
+            Field::new("image", DataType::Utf8, true),
+            Arc::new(StringArray::from(vec![Some("s3://bucket/key"), None])),
+        );
+        let coerced = coerce(batch, &blob_table_schema()).await;
+        let image = image_struct(&coerced);
+        let uri: &StringArray = image
+            .column_by_name("uri")
+            .unwrap()
+            .as_any()
+            .downcast_ref()
+            .unwrap();
+        assert_eq!(uri.value(0), "s3://bucket/key");
+        assert!(image.column_by_name("data").unwrap().is_null(0));
+        assert!(uri.is_null(1));
+    }
+
+    #[tokio::test]
+    async fn large_utf8_string_coerces_into_four_child_blob_layout() {
+        use arrow_array::LargeStringArray;
+
+        let table_schema = Schema::new(vec![
+            Field::new("id", DataType::Int64, false),
+            wide_blob_field("image"),
+        ]);
+        let batch = batch_with_image(
+            Field::new("image", DataType::LargeUtf8, true),
+            Arc::new(LargeStringArray::from(vec!["file:///tmp/blob.bin"])),
+        );
+        let coerced = coerce(batch, &table_schema).await;
+        let image = image_struct(&coerced);
+        assert_eq!(image.num_columns(), 4);
+        let uri: &StringArray = image
+            .column_by_name("uri")
+            .unwrap()
+            .as_any()
+            .downcast_ref()
+            .unwrap();
+        assert_eq!(uri.value(0), "file:///tmp/blob.bin");
+        assert!(image.column_by_name("data").unwrap().is_null(0));
+        assert!(image.column_by_name("position").unwrap().is_null(0));
+        assert!(image.column_by_name("size").unwrap().is_null(0));
+    }
+
+    #[tokio::test]
+    async fn utf8_view_string_coerces_to_uri_child() {
+        let batch = batch_with_image(
+            Field::new("image", DataType::Utf8View, true),
+            Arc::new(StringViewArray::from(vec![Some("s3://bucket/view-key")])),
+        );
+        let coerced = coerce(batch, &blob_table_schema()).await;
+        let image = image_struct(&coerced);
+        let uri: &StringArray = image
+            .column_by_name("uri")
+            .unwrap()
+            .as_any()
+            .downcast_ref()
+            .unwrap();
+        assert_eq!(uri.value(0), "s3://bucket/view-key");
+        assert!(image.column_by_name("data").unwrap().is_null(0));
+    }
+
    #[tokio::test]
    async fn blob_metadata_survives_cast_of_sibling_column() {
        let batch = RecordBatch::try_new(
@@ -32,6 +32,10 @@ struct DatasetState {
    /// `Some(version)` = pinned to a specific version (time travel),
    /// `None` = tracking latest.
    pinned_version: Option<u64>,
+    /// Whether the pin is an internal query snapshot rather than user-visible
+    /// time travel. Query snapshots remain read-only but preserve MemWAL read
+    /// semantics.
+    query_snapshot: bool,
 }

 #[derive(Debug, Clone)]
@@ -70,6 +74,7 @@ impl DatasetConsistencyWrapper {
            state: Arc::new(Mutex::new(DatasetState {
                dataset,
                pinned_version: None,
+                query_snapshot: false,
            })),
            consistency,
            shard_writer: Arc::new(ShardWriterCache::default()),
@@ -93,6 +98,36 @@ impl DatasetConsistencyWrapper {
        wrapper
    }

+    /// Create an independent read-only wrapper pinned to the current dataset
+    /// while retaining this wrapper's live MemWAL read context.
+    pub async fn new_query_snapshot(&self) -> Result<Self> {
+        // Apply the configured consistency policy before taking the snapshot.
+        // The returned dataset is intentionally discarded: a checkout may race
+        // after this await, so the dataset and its pin provenance must instead
+        // be cloned together from one authoritative state sample below.
+        self.get().await?;
+
+        let (dataset, query_snapshot) = {
+            let state = self.state.lock()?;
+            // Preserve user time travel so the MemWAL safety guard still sees
+            // it. Latest and already-internal snapshots remain internal pins.
+            (
+                state.dataset.clone(),
+                state.query_snapshot || state.pinned_version.is_none(),
+            )
+        };
+        let version = dataset.version().version;
+        Ok(Self {
+            state: Arc::new(Mutex::new(DatasetState {
+                dataset,
+                pinned_version: Some(version),
+                query_snapshot,
+            })),
+            consistency: ConsistencyMode::Lazy,
+            shard_writer: self.shard_writer.clone(),
+        })
+    }
+
    /// The MemWAL `ShardWriter` cache co-located with this dataset.
    pub(crate) fn shard_writer(&self) -> &Arc<ShardWriterCache> {
        &self.shard_writer
@@ -169,6 +204,7 @@ impl DatasetConsistencyWrapper {
        let mut state = self.state.lock()?;
        state.dataset = Arc::new(new_dataset);
        state.pinned_version = None;
+        state.query_snapshot = false;
        drop(state);
        if let ConsistencyMode::Eventual(bg_cache) = &self.consistency {
            bg_cache.invalidate();
@@ -202,10 +238,10 @@ impl DatasetConsistencyWrapper {

    /// Returns the version, if in time travel mode, or None otherwise.
    pub fn time_travel_version(&self) -> Option<u64> {
-        self.state
-            .lock()
-            .unwrap_or_else(|e| e.into_inner())
-            .pinned_version
+        let state = self.state.lock().unwrap_or_else(|e| e.into_inner());
+        (!state.query_snapshot)
+            .then_some(state.pinned_version)
+            .flatten()
    }

    /// Convert into a wrapper in latest version mode.
@@ -225,6 +261,7 @@ impl DatasetConsistencyWrapper {
        if state.pinned_version.is_some() {
            state.dataset = Arc::new(new_dataset);
            state.pinned_version = None;
+            state.query_snapshot = false;
        }
        drop(state);
        if let ConsistencyMode::Eventual(bg_cache) = &self.consistency {
@@ -260,6 +297,7 @@ impl DatasetConsistencyWrapper {
        let mut state = self.state.lock()?;
        state.dataset = Arc::new(new_dataset);
        state.pinned_version = Some(version_value);
+        state.query_snapshot = false;
        Ok(())
    }

@@ -461,6 +499,29 @@ mod tests {
        assert_eq!(wrapper.time_travel_version(), Some(1));
    }

+    #[tokio::test]
+    async fn test_query_snapshot_samples_dataset_and_pin_together() {
+        let dir = tempfile::tempdir().unwrap();
+        let uri = dir.path().to_str().unwrap();
+        let ds = create_test_dataset(uri).await;
+
+        let wrapper = DatasetConsistencyWrapper::new_latest(ds, None);
+        wrapper.as_time_travel(1u64).await.unwrap();
+        let stale_time_travel_dataset = wrapper.get().await.unwrap();
+
+        append_to_dataset(uri).await;
+        wrapper.as_latest().await.unwrap();
+
+        let snapshot = wrapper.new_query_snapshot().await.unwrap();
+        let snapshot_dataset = snapshot.get().await.unwrap();
+        assert_eq!(snapshot_dataset.version().version, 2);
+        assert_ne!(
+            snapshot_dataset.version().version,
+            stale_time_travel_dataset.version().version
+        );
+        assert_eq!(snapshot.time_travel_version(), None);
+    }
+
    #[tokio::test]
    async fn test_as_latest_from_time_travel() {
        let dir = tempfile::tempdir().unwrap();
@@ -1056,6 +1056,44 @@ mod lsm_tests {
        );
    }

+    #[tokio::test]
+    async fn query_snapshot_preserves_lsm_read_semantics() {
+        let dir = tempdir().unwrap();
+        let table = id_value_table(&dir).await;
+        table
+            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .await
+            .unwrap();
+        lsm_upsert(&table, vec![4, 5]).await;
+
+        let snapshot = table.query_snapshot().await.unwrap();
+        let rows = collect_id_value(snapshot.query().execute().await.unwrap()).await;
+        assert_eq!(
+            rows.iter().map(|(id, _)| *id).collect::<Vec<_>>(),
+            vec![1, 2, 3, 4, 5]
+        );
+    }
+
+    #[tokio::test]
+    async fn query_snapshot_preserves_time_travel_lsm_guard() {
+        let dir = tempdir().unwrap();
+        let table = id_value_table(&dir).await;
+        table
+            .set_lsm_write_spec(LsmWriteSpec::unsharded())
+            .await
+            .unwrap();
+        lsm_upsert(&table, vec![4]).await;
+
+        let version = table.version().await.unwrap();
+        table.checkout(version).await.unwrap();
+        let direct_error = table.query().execute().await.err().unwrap();
+        assert!(matches!(direct_error, Error::NotSupported { .. }));
+
+        let snapshot = table.query_snapshot().await.unwrap();
+        let snapshot_error = snapshot.query().execute().await.err().unwrap();
+        assert!(matches!(snapshot_error, Error::NotSupported { .. }));
+    }
+
    #[tokio::test]
    async fn lsm_read_dedup_newest_wins() {
        let dir = tempdir().unwrap();
@@ -70,6 +70,9 @@ async fn can_execute_namespace_query(table: &NativeTable, query: &AnyQuery) -> R
        .contains(&NamespaceClientPushdownOperation::QueryTable)
        && table.namespace_client.is_some()
        && table.dataset.current_branch().is_none()
+        // NsQueryTableRequest has no version field, so a pushed-down query would
+        // read latest and ignore the pin.
+        && table.dataset.time_travel_version().is_none()
        && !requires_local_namespace_execution(query))
    {
        return Ok(false);
@@ -694,6 +697,7 @@ mod tests {

    use super::*;
    use crate::query::{QueryExecutionOptions, QueryRequest};
+    use crate::table::BaseTable;

    fn fixed_size_list_array(values: Vec<f32>, dimension: i32) -> FixedSizeListArray {
        FixedSizeListArray::try_new_from_values(Float32Array::from(values), dimension).unwrap()
@@ -886,10 +890,56 @@ mod tests {

        async fn query_table(&self, _request: NsQueryTableRequest) -> lance::Result<bytes::Bytes> {
            self.query_table_calls.fetch_add(1, Ordering::SeqCst);
-            panic!("approx_mode queries must not be pushed down to namespace query_table");
+            panic!("query must not be pushed down to namespace query_table");
        }
    }

+    #[tokio::test]
+    async fn test_execute_query_pinned_snapshot_with_namespace_pushdown_runs_locally() {
+        use crate::connect;
+        use arrow_array::{Int32Array, RecordBatch};
+        use arrow_schema::{DataType, Field, Schema};
+
+        let conn = connect("memory://").execute().await.unwrap();
+        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
+        let batch = RecordBatch::try_new(
+            schema,
+            vec![Arc::new(Int32Array::from(vec![1, 2, 3, 4, 5]))],
+        )
+        .unwrap();
+        let table = conn
+            .create_table("test_pinned_namespace_fallback", vec![batch])
+            .execute()
+            .await
+            .unwrap();
+
+        let namespace_client = Arc::new(CountingNamespaceClient::default());
+        let mut native_table = table.as_native().unwrap().clone();
+        native_table.namespace_client = Some(namespace_client.clone());
+        native_table
+            .pushdown_operations
+            .insert(NamespaceClientPushdownOperation::QueryTable);
+
+        let snapshot = native_table.checkout_current().await.unwrap();
+        let snapshot = snapshot.as_any().downcast_ref::<NativeTable>().unwrap();
+        assert!(snapshot.dataset.time_travel_version().is_some());
+
+        let query = AnyQuery::Query(QueryRequest {
+            filter: Some(QueryFilter::Sql("id > 3".to_string())),
+            ..Default::default()
+        });
+        let stream = execute_query(snapshot, &query, QueryExecutionOptions::default())
+            .await
+            .unwrap();
+        let batches = stream.try_collect::<Vec<_>>().await.unwrap();
+
+        assert_eq!(
+            batches.iter().map(|batch| batch.num_rows()).sum::<usize>(),
+            2
+        );
+        assert_eq!(namespace_client.query_table_calls.load(Ordering::SeqCst), 0);
+    }
+
    #[tokio::test]
    async fn test_execute_query_approx_mode_with_namespace_pushdown_runs_locally() {
        use crate::connect;
@@ -1006,6 +1056,37 @@ mod tests {
        assert_eq!(namespace_client.query_table_calls.load(Ordering::SeqCst), 0);
    }

+    #[tokio::test]
+    async fn test_query_snapshot_disables_namespace_pushdown() {
+        use crate::connect;
+        use crate::table::BaseTable;
+        use arrow_array::{Int32Array, RecordBatch};
+        use arrow_schema::{DataType, Field, Schema};
+
+        let conn = connect("memory://").execute().await.unwrap();
+        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
+        let batch =
+            RecordBatch::try_new(schema, vec![Arc::new(Int32Array::from(vec![1, 2, 3]))]).unwrap();
+        let table = conn
+            .create_table("test_snapshot_namespace_fallback", vec![batch])
+            .execute()
+            .await
+            .unwrap();
+        let mut native_table = table.as_native().unwrap().clone();
+        native_table.namespace_client = Some(Arc::new(CountingNamespaceClient::default()));
+        native_table
+            .pushdown_operations
+            .insert(NamespaceClientPushdownOperation::QueryTable);
+
+        let snapshot = BaseTable::query_snapshot(&native_table).await.unwrap();
+        let snapshot = snapshot.as_any().downcast_ref::<NativeTable>().unwrap();
+        assert!(
+            !can_execute_namespace_query(snapshot, &AnyQuery::Query(QueryRequest::default()),)
+                .await
+                .unwrap()
+        );
+    }
+
    #[tokio::test]
    async fn test_create_plan_multivector_structure() {
        use arrow_array::{Float32Array, RecordBatch};
@@ -298,7 +298,7 @@ async fn build_read_context(
    for shard_id in shard_ids {
        let manifest_store =
            ShardManifestStore::new(store.clone(), &base_path, shard_id, scan_batch_size);
-        if let Some(manifest) = manifest_store.read_latest().await? {
+        if let Some(manifest) = manifest_store.latest().await? {
            snapshots.push(snapshot_from_manifest(shard_id, &manifest, &exclude));
        }
    }
@@ -5,12 +5,14 @@ use std::sync::Arc;

 use arrow_array::{
    Array, ArrayRef, BinaryArray, Int64Array, LargeBinaryArray, RecordBatch, StringArray,
-    StructArray, UInt64Array,
+    StructArray, UInt64Array, new_null_array,
 };
 use arrow_schema::{DataType, Field, Fields, Schema};
 use futures::TryStreamExt;
 use lance::Dataset;
+use lance::dataset::WriteParams;
 use lance_file::version::{ConcreteFileVersion, LanceFileVersion};
+use lance_table::format::BasePath;
 use lancedb::{
    Connection, Error, Result, Table,
    blob::{BlobRangeRequest, blob},
@@ -19,7 +21,7 @@ use lancedb::{
        ListingDatabaseOptions, NewTableConfig, OPT_NEW_TABLE_ENABLE_STABLE_ROW_IDS,
    },
    query::{ExecutableQuery, QueryBase},
-    table::{AddDataMode, CompactionOptions, OptimizeAction, OptimizeStats},
+    table::{AddDataMode, CompactionOptions, OptimizeAction, OptimizeStats, WriteOptions},
 };
 use tempfile::tempdir;

@@ -261,11 +263,11 @@ async fn add_rejects_uncoercible_blob_input() -> Result<()> {
    let batch = RecordBatch::try_new(
        Arc::new(Schema::new(vec![
            Field::new("id", DataType::Int64, false),
-            Field::new("image", DataType::Utf8, true),
+            Field::new("image", DataType::Int64, true),
        ])),
        vec![
            Arc::new(Int64Array::from(vec![1])),
-            Arc::new(StringArray::from(vec!["not bytes"])),
+            Arc::new(Int64Array::from(vec![42])),
        ],
    )
    .unwrap();
@@ -1332,3 +1334,223 @@ async fn optimize_preserves_blob_v2_null_and_empty_distinction() -> Result<()> {
    );
    Ok(())
 }
+
+fn uri_struct_batch(id: i64, uri: &str) -> RecordBatch {
+    let image_field = blob("image", true);
+    let DataType::Struct(child_fields) = image_field.data_type().clone() else {
+        unreachable!("blob field is a struct");
+    };
+    let children: Vec<ArrayRef> = child_fields
+        .iter()
+        .map(|field| match field.name().as_str() {
+            "uri" => Arc::new(StringArray::from(vec![Some(uri)])) as ArrayRef,
+            _ => new_null_array(field.data_type(), 1),
+        })
+        .collect();
+    let image = StructArray::new(child_fields, children, None);
+    RecordBatch::try_new(
+        Arc::new(Schema::new(vec![
+            Field::new("id", DataType::Int64, false),
+            image_field,
+        ])),
+        vec![Arc::new(Int64Array::from(vec![id])), Arc::new(image)],
+    )
+    .unwrap()
+}
+
+fn uri_string_batch(id: i64, uri: &str) -> RecordBatch {
+    RecordBatch::try_new(
+        Arc::new(Schema::new(vec![
+            Field::new("id", DataType::Int64, false),
+            Field::new("image", DataType::Utf8, true),
+        ])),
+        vec![
+            Arc::new(Int64Array::from(vec![id])),
+            Arc::new(StringArray::from(vec![Some(uri)])),
+        ],
+    )
+    .unwrap()
+}
+
+fn write_payload_file_uri(dir: &std::path::Path, name: &str, payload: &[u8]) -> String {
+    let path = dir.join(name);
+    std::fs::write(&path, payload).unwrap();
+    url::Url::from_file_path(&path).unwrap().to_string()
+}
+
+#[tokio::test]
+async fn external_uri_struct_round_trips_with_flag() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().join("db").to_str().unwrap())
+        .execute()
+        .await?;
+    let payload: &[u8] = b"external-struct-payload";
+    let uri = write_payload_file_uri(tmp.path(), "payload.bin", payload);
+    let table = db
+        .create_empty_table("t", blob_table_schema())
+        .execute()
+        .await?;
+
+    table
+        .add(uri_struct_batch(1, &uri))
+        .allow_external_blob_outside_bases(true)
+        .execute()
+        .await?;
+
+    let ids = collect_row_ids(&table).await?;
+    let bytes = table.fetch_blobs("image", &ids).await?;
+    assert_eq!(bytes.value(0), payload);
+    Ok(())
+}
+
+#[tokio::test]
+async fn external_uri_add_requires_opt_in() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().join("db").to_str().unwrap())
+        .execute()
+        .await?;
+    let uri = write_payload_file_uri(tmp.path(), "payload.bin", b"unreachable");
+    let table = db
+        .create_empty_table("t", blob_table_schema())
+        .execute()
+        .await?;
+
+    let err = table
+        .add(uri_struct_batch(1, &uri))
+        .execute()
+        .await
+        .unwrap_err();
+
+    assert!(
+        err.to_string()
+            .contains("allow_external_blob_outside_bases"),
+        "got: {err}"
+    );
+    assert_eq!(table.count_rows(None).await?, 0);
+    Ok(())
+}
+
+#[tokio::test]
+async fn string_uri_input_round_trips_as_external_reference() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().join("db").to_str().unwrap())
+        .execute()
+        .await?;
+    let payload: &[u8] = b"external-string-payload";
+    let uri = write_payload_file_uri(tmp.path(), "payload.bin", payload);
+    let table = db
+        .create_empty_table("t", blob_table_schema())
+        .execute()
+        .await?;
+
+    table
+        .add(uri_string_batch(1, &uri))
+        .allow_external_blob_outside_bases(true)
+        .execute()
+        .await?;
+
+    let ids = collect_row_ids(&table).await?;
+    let bytes = table.fetch_blobs("image", &ids).await?;
+    assert_eq!(bytes.value(0), payload);
+
+    let files = table.fetch_blob_files("image", &ids).await?;
+    let file = files[0].as_ref().expect("missing blob file");
+    assert_eq!(file.uri(), Some(uri.as_str()));
+    Ok(())
+}
+
+#[tokio::test]
+async fn string_uri_inside_registered_base_does_not_need_the_flag() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db_path = tmp.path().join("db");
+    let external_base = tmp.path().join("external_base");
+    let object_dir = external_base.join("objects");
+    std::fs::create_dir_all(&object_dir).unwrap();
+    let payload: &[u8] = b"mapped-in-base";
+    let object_path = object_dir.join("mapped.bin");
+    std::fs::write(&object_path, payload).unwrap();
+    let object_uri = url::Url::from_file_path(&object_path).unwrap().to_string();
+    let base_uri = url::Url::from_file_path(&external_base)
+        .unwrap()
+        .to_string();
+
+    let db = connect(db_path.to_str().unwrap()).execute().await?;
+    let table = db
+        .create_empty_table("t", blob_table_schema())
+        .write_options(WriteOptions {
+            lance_write_params: Some(WriteParams {
+                initial_bases: Some(vec![BasePath {
+                    id: 1,
+                    name: Some("external".to_string()),
+                    path: base_uri,
+                    is_dataset_root: false,
+                }]),
+                ..Default::default()
+            }),
+        })
+        .execute()
+        .await?;
+
+    table
+        .add(uri_string_batch(1, &object_uri))
+        .execute()
+        .await?;
+
+    let ids = collect_row_ids(&table).await?;
+    let bytes = table.fetch_blobs("image", &ids).await?;
+    assert_eq!(bytes.value(0), payload);
+    Ok(())
+}
+
+#[tokio::test]
+async fn external_uri_rows_mix_with_inline_rows() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().join("db").to_str().unwrap())
+        .execute()
+        .await?;
+    let external_payload: &[u8] = b"external-bytes";
+    let uri = write_payload_file_uri(tmp.path(), "payload.bin", external_payload);
+    let table =
+        create_inline_blob_table(&db, "t", &[1], &[Some(b"inline-bytes".as_slice())]).await?;
+
+    table
+        .add(uri_string_batch(2, &uri))
+        .allow_external_blob_outside_bases(true)
+        .execute()
+        .await?;
+
+    let pairs = collect_id_rowid(&table).await?;
+    let row_ids: Vec<u64> = pairs.iter().map(|(_, r)| *r).collect();
+    let bytes = table.fetch_blobs("image", &row_ids).await?;
+    for (i, (id, _)) in pairs.iter().enumerate() {
+        match id {
+            1 => assert_eq!(bytes.value(i), b"inline-bytes"),
+            2 => assert_eq!(bytes.value(i), external_payload),
+            _ => unreachable!(),
+        }
+    }
+    Ok(())
+}
+
+#[tokio::test]
+async fn malformed_string_uri_is_rejected_at_write() -> Result<()> {
+    let tmp = tempdir().unwrap();
+    let db = connect(tmp.path().join("db").to_str().unwrap())
+        .execute()
+        .await?;
+    let table = db
+        .create_empty_table("t", blob_table_schema())
+        .execute()
+        .await?;
+
+    let err = table
+        .add(uri_string_batch(1, "not a uri"))
+        .allow_external_blob_outside_bases(true)
+        .execute()
+        .await
+        .unwrap_err();
+
+    assert!(err.to_string().contains("not a uri"), "got: {err}");
+    assert_eq!(table.count_rows(None).await?, 0);
+    Ok(())
+}
@@ -20,25 +20,6 @@ fn job_result(name: &str) -> Value {
    serde_json::from_str::<Value>(&fixture(name)).expect("remote Job fixture")["result"].clone()
 }

-fn assert_no_secret_values(value: &Value) {
-    match value {
-        Value::Object(values) => {
-            for (key, value) in values {
-                assert!(
-                    !matches!(
-                        key.as_str(),
-                        "secret_value" | "secret_values" | "resolved_secret" | "resolved_secrets"
-                    ),
-                    "client canonical value must not model resolved secret material"
-                );
-                assert_no_secret_values(value);
-            }
-        }
-        Value::Array(values) => values.iter().for_each(assert_no_secret_values),
-        _ => {}
-    }
-}
-
 #[test]
 fn function_version_job_result_matches_shared_canonical_golden() {
    let result = job_result("remote_function_job.json");
@@ -47,7 +28,6 @@ fn function_version_job_result_matches_shared_canonical_golden() {
    assert_eq!(version.name(), "embed");
    assert_eq!(version.version(), "fv_01K3EXACT");
    assert_eq!(version.runtime_digest(), "sha256:runtime");
-    assert_eq!(version.required_secrets(), &["HF_TOKEN"]);
    assert_eq!(
        version.to_canonical_json().expect("canonical JSON"),
        fixture("remote_function_version.canonical.json").trim()
@@ -84,7 +64,6 @@ fn application_and_binding_match_shared_remote_goldens() {

    let binding = FunctionBinding::from_json(&fixture("remote_function_binding.json"))
        .expect("binding fixture");
-    assert_eq!(binding.revision(), 3);
    assert_eq!(binding.function().version, "fv_01K3TEXT");
    assert_eq!(binding.outputs()[0].output_ordinal, 0);
    assert_eq!(binding.outputs()[1].output_ordinal, 1);
@@ -163,21 +142,3 @@ fn floating_point_application_literals_are_rejected_consistently() {
            .contains("floating-point Function literals")
    );
 }
-
-#[test]
-fn canonical_client_values_contain_secret_names_only() {
-    let result = job_result("remote_function_job.json");
-    let version = FunctionVersion::from_json(&result.to_string()).expect("FunctionVersion result");
-    let canonical: Value = serde_json::from_str(
-        &version
-            .to_canonical_json()
-            .expect("canonical FunctionVersion"),
-    )
-    .expect("canonical JSON");
-
-    assert_eq!(
-        canonical["required_secrets"],
-        serde_json::json!(["HF_TOKEN"])
-    );
-    assert_no_secret_values(&canonical);
-}
@@ -6,7 +6,6 @@ use std::path::PathBuf;

 use lancedb::Error;
 use lancedb::function::FunctionRegistrationRequest;
-use serde_json::Value;

 fn fixture(name: &str) -> String {
    let path = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
@@ -15,25 +14,6 @@ fn fixture(name: &str) -> String {
    fs::read_to_string(path).expect("fixture must be readable")
 }

-fn assert_no_secret_values(value: &Value) {
-    match value {
-        Value::Object(values) => {
-            for (key, value) in values {
-                assert!(
-                    !matches!(
-                        key.as_str(),
-                        "secret_value" | "secret_values" | "resolved_secret" | "resolved_secrets"
-                    ),
-                    "registration requests must not model resolved secret material"
-                );
-                assert_no_secret_values(value);
-            }
-        }
-        Value::Array(values) => values.iter().for_each(assert_no_secret_values),
-        _ => {}
-    }
-}
-
 #[test]
 fn registration_request_matches_shared_canonical_golden() {
    let request = FunctionRegistrationRequest::from_json(&fixture(
@@ -42,16 +22,10 @@ fn registration_request_matches_shared_canonical_golden() {
    .expect("registration request");
    assert_eq!(request.name, "normalize_score");
    assert_eq!(request.artifact.adapter.kind, "scalar_to_arrow_batch");
-    assert_eq!(request.required_secrets, ["API_TOKEN"]);
    assert_eq!(
        request.to_canonical_json().expect("canonical request"),
        fixture("remote_function_registration_request.canonical.json").trim()
    );
-
-    let value: Value =
-        serde_json::from_str(&request.to_canonical_json().expect("canonical request"))
-            .expect("request JSON");
-    assert_no_secret_values(&value);
 }

 #[tokio::test]
@@ -0,0 +1,333 @@
+{
+  "valid": [
+    {
+      "arrow_type": "bool",
+      "json": {
+        "type": "bool"
+      }
+    },
+    {
+      "arrow_type": "int8",
+      "json": {
+        "type": "int8"
+      }
+    },
+    {
+      "arrow_type": "int16",
+      "json": {
+        "type": "int16"
+      }
+    },
+    {
+      "arrow_type": "int32",
+      "json": {
+        "type": "int32"
+      }
+    },
+    {
+      "arrow_type": "int64",
+      "json": {
+        "type": "int64"
+      }
+    },
+    {
+      "arrow_type": "uint8",
+      "json": {
+        "type": "uint8"
+      }
+    },
+    {
+      "arrow_type": "uint16",
+      "json": {
+        "type": "uint16"
+      }
+    },
+    {
+      "arrow_type": "uint32",
+      "json": {
+        "type": "uint32"
+      }
+    },
+    {
+      "arrow_type": "uint64",
+      "json": {
+        "type": "uint64"
+      }
+    },
+    {
+      "arrow_type": "float16",
+      "json": {
+        "type": "float16"
+      }
+    },
+    {
+      "arrow_type": "float32",
+      "json": {
+        "type": "float32"
+      }
+    },
+    {
+      "arrow_type": "float64",
+      "json": {
+        "type": "float64"
+      }
+    },
+    {
+      "arrow_type": "utf8",
+      "json": {
+        "type": "utf8"
+      }
+    },
+    {
+      "arrow_type": "binary",
+      "json": {
+        "type": "binary"
+      }
+    },
+    {
+      "arrow_type": "date32",
+      "json": {
+        "type": "date32"
+      }
+    },
+    {
+      "arrow_type": "date64",
+      "json": {
+        "type": "date64"
+      }
+    },
+    {
+      "arrow_type": "list<float32>",
+      "json": {
+        "type": "list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "float32"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "large_list<float32>",
+      "json": {
+        "type": "large_list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "float32"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "list<int64>",
+      "json": {
+        "type": "list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "int64"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "large_list<int64>",
+      "json": {
+        "type": "large_list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "int64"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "list<utf8>",
+      "json": {
+        "type": "list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "utf8"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "large_list<utf8>",
+      "json": {
+        "type": "large_list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "utf8"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "fixed_size_list<float32, 384>",
+      "json": {
+        "type": "fixed_size_list",
+        "length": 384,
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "float32"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "fixed_size_list<float16, 8>",
+      "json": {
+        "type": "fixed_size_list",
+        "length": 8,
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "float16"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "fixed_size_list<uint8, 1>",
+      "json": {
+        "type": "fixed_size_list",
+        "length": 1,
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "uint8"
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "list<list<float32>>",
+      "json": {
+        "type": "list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "list",
+              "fields": [
+                {
+                  "name": "item",
+                  "nullable": false,
+                  "type": {
+                    "type": "float32"
+                  }
+                }
+              ]
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "fixed_size_list<list<int32>, 2>",
+      "json": {
+        "type": "fixed_size_list",
+        "length": 2,
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "list",
+              "fields": [
+                {
+                  "name": "item",
+                  "nullable": false,
+                  "type": {
+                    "type": "int32"
+                  }
+                }
+              ]
+            }
+          }
+        ]
+      }
+    },
+    {
+      "arrow_type": "list<fixed_size_list<float32, 3>>",
+      "json": {
+        "type": "list",
+        "fields": [
+          {
+            "name": "item",
+            "nullable": false,
+            "type": {
+              "type": "fixed_size_list",
+              "length": 3,
+              "fields": [
+                {
+                  "name": "item",
+                  "nullable": false,
+                  "type": {
+                    "type": "float32"
+                  }
+                }
+              ]
+            }
+          }
+        ]
+      }
+    }
+  ],
+  "server_only": [
+    {
+      "arrow_type": "null",
+      "json": {
+        "type": "null"
+      }
+    }
+  ],
+  "invalid": [
+    "",
+    "list<>",
+    "list<float32",
+    "fixed_size_list<float32>[3]",
+    "fixed_size_list<float32>",
+    "fixed_size_list<float32, 0>",
+    "fixed_size_list<float32, x>",
+    "map<utf8, int32>",
+    "decimal128(10, 2)",
+    "timestamp[us]",
+    "struct<a: int32>"
+  ]
+}
@@ -0,0 +1,78 @@
+{
+  "new_columns": [
+    {
+      "name": "embedding",
+      "all_null": true
+    }
+  ],
+  "function": {
+    "application": {
+      "function": {
+        "name": "embed",
+        "version": "fv_01K3EXACT"
+      },
+      "inputs": [
+        {
+          "parameter": "text",
+          "kind": "column",
+          "value": {
+            "path": "description"
+          }
+        }
+      ],
+      "output": {
+        "kind": "scalar",
+        "arrow_type": "fixed_size_list<float32, 3>",
+        "nullable": false
+      }
+    },
+    "binding_metadata_version": 1,
+    "input_bindings": [
+      {
+        "parameter": "text",
+        "field_path": "description",
+        "arrow_type": "utf8",
+        "nullable": true
+      }
+    ],
+    "input_schema": {
+      "fields": [
+        {
+          "name": "text",
+          "nullable": true,
+          "type": {
+            "type": "utf8"
+          }
+        }
+      ]
+    },
+    "output_schema": {
+      "fields": [
+        {
+          "name": "embedding",
+          "nullable": true,
+          "type": {
+            "type": "fixed_size_list",
+            "length": 3,
+            "fields": [
+              {
+                "name": "item",
+                "nullable": false,
+                "type": {
+                  "type": "float32"
+                }
+              }
+            ]
+          }
+        }
+      ]
+    },
+    "outputs": [
+      {
+        "result_field": "$value",
+        "output_name": "embedding",
+        "output_ordinal": 0
+      }
+    ]
+  }
+}
@@ -1 +1 @@
-{"columns":{"normalized_text":"search_text","token_count":"search_token_count"},"function":{"name":"text_features","version":"fv_01K3TEXT"},"group_id":"fg_01K3TEXT","inputs":[{"kind":"column","parameter":"title","value":{"path":"title"}},{"kind":"column","parameter":"body","value":{"path":"body"}}],"output":{"fields":[{"arrow_type":"utf8","name":"normalized_text","nullable":false},{"arrow_type":"int64","name":"token_count","nullable":false}],"kind":"named_struct"}}
+{"columns":{"normalized_text":"search_text","token_count":"search_token_count"},"function":{"name":"text_features","version":"fv_01K3TEXT"},"inputs":[{"kind":"column","parameter":"title","value":{"path":"title"}},{"kind":"column","parameter":"body","value":{"path":"body"}}],"output":{"fields":[{"arrow_type":"utf8","name":"normalized_text","nullable":false},{"arrow_type":"int64","name":"token_count","nullable":false}],"kind":"named_struct"}}
@@ -11,7 +11,6 @@
      {"name": "token_count", "arrow_type": "int64", "nullable": false}
    ]
  },
-  "group_id": "fg_01K3TEXT",
  "columns": {
    "normalized_text": "search_text",
    "token_count": "search_token_count"
@@ -3,6 +3,5 @@
  "inputs": [
    {"parameter": "threshold", "kind": "literal", "value": 1e-7}
  ],
-  "output": {"kind": "scalar", "arrow_type": "bool", "nullable": false},
-  "group_id": "fg_01K3FLOAT"
+  "output": {"kind": "scalar", "arrow_type": "bool", "nullable": false}
 }
@@ -1 +1 @@
-{"binding_id":"fb_01K3TEXT","function":{"name":"text_features","version":"fv_01K3TEXT"},"group_id":"fg_01K3TEXT","input_schema":{"fields":[{"name":"title","nullable":true,"type":{"type":"utf8"}},{"name":"body","nullable":true,"type":{"type":"utf8"}}]},"inputs":[{"arrow_type":"utf8","field_id":11,"field_path":"title","nullable":true,"parameter":"title"},{"arrow_type":"utf8","field_id":12,"field_path":"body","nullable":true,"parameter":"body"}],"output_schema":{"fields":[{"name":"search_text","nullable":true,"type":{"type":"utf8"}},{"name":"search_token_count","nullable":true,"type":{"type":"int64"}}]},"outputs":[{"arrow_type":"utf8","nullable":false,"output_field_id":21,"output_name":"search_text","output_ordinal":0,"result_field":"normalized_text"},{"arrow_type":"int64","nullable":false,"output_field_id":22,"output_name":"search_token_count","output_ordinal":1,"result_field":"token_count"}],"revision":3}
+{"binding_id":"fb_01K3TEXT","function":{"name":"text_features","version":"fv_01K3TEXT"},"input_schema":{"fields":[{"name":"title","nullable":true,"type":{"type":"utf8"}},{"name":"body","nullable":true,"type":{"type":"utf8"}}]},"inputs":[{"arrow_type":"utf8","field_id":11,"field_path":"title","nullable":true,"parameter":"title"},{"arrow_type":"utf8","field_id":12,"field_path":"body","nullable":true,"parameter":"body"}],"output_schema":{"fields":[{"name":"search_text","nullable":true,"type":{"type":"utf8"}},{"name":"search_token_count","nullable":true,"type":{"type":"int64"}}]},"outputs":[{"arrow_type":"utf8","nullable":false,"output_field_id":21,"output_name":"search_text","output_ordinal":0,"result_field":"normalized_text"},{"arrow_type":"int64","nullable":false,"output_field_id":22,"output_name":"search_token_count","output_ordinal":1,"result_field":"token_count"}]}
@@ -1,8 +1,6 @@
 {
  "binding_id": "fb_01K3TEXT",
-  "revision": 3,
  "function": {"name": "text_features", "version": "fv_01K3TEXT"},
-  "group_id": "fg_01K3TEXT",
  "inputs": [
    {"parameter": "title", "field_id": 11, "field_path": "title", "arrow_type": "utf8", "nullable": true},
    {"parameter": "body", "field_id": 12, "field_path": "body", "arrow_type": "utf8", "nullable": true}
@@ -23,5 +21,5 @@
      {"name": "search_token_count", "nullable": true, "type": {"type": "int64"}}
    ]
  },
-  "future_binding": {"metadata_revision": 1}
+  "future_binding": {"mode": "managed"}
 }
@@ -24,7 +24,6 @@
    },
    "runtime_digest": "sha256:runtime",
    "environment_digest": "sha256:environment",
-    "required_secrets": ["HF_TOKEN"],
    "created_at": "2026-08-21T00:00:00Z"
  },
  "future_job": {"trace_id": "trace-1"}
@@ -1 +1 @@
-{"artifact":{"adapter":{"kind":"scalar_to_arrow_batch","version":1},"content":{"data":"ZnJvbSBfX2Z1dHVyZV9fIGltcG9ydCBhbm5vdGF0aW9ucwoKZGVmIG5vcm1hbGl6ZV9zY29yZSh2YWx1ZTogZmxvYXQpIC0+IGZsb2F0OgogICAgcmV0dXJuIHZhbHVlIC8gMTAwLjAK","encoding":"base64"},"digest":"sha256:760784bdcef57b802f389b97804cc0b618aae39e86044733451bcd0b13089a7f","entrypoint":"normalize_score","kind":"python_callable"},"name":"normalize_score","required_secrets":["API_TOKEN"],"runtime":{"env":{"MODE":"test"},"environment":{"kind":"pip","packages":["numpy>=2"]},"kind":"python","python_version":"3.12"},"signature":{"inputs":[{"arrow_type":"float64","name":"value","nullable":false}],"output":{"arrow_type":"float64","kind":"scalar","nullable":false}}}
+{"artifact":{"adapter":{"kind":"scalar_to_arrow_batch","version":1},"content":{"data":"ZnJvbSBfX2Z1dHVyZV9fIGltcG9ydCBhbm5vdGF0aW9ucwoKZGVmIG5vcm1hbGl6ZV9zY29yZSh2YWx1ZTogZmxvYXQpIC0+IGZsb2F0OgogICAgcmV0dXJuIHZhbHVlIC8gMTAwLjAK","encoding":"base64"},"digest":"sha256:760784bdcef57b802f389b97804cc0b618aae39e86044733451bcd0b13089a7f","entrypoint":"normalize_score","kind":"python_callable"},"name":"normalize_score","runtime":{"env":{"MODE":"test"},"environment":{"kind":"pip","packages":["numpy>=2"]},"kind":"python","python_version":"3.12"},"signature":{"inputs":[{"arrow_type":"float64","name":"value","nullable":false}],"output":{"arrow_type":"float64","kind":"scalar","nullable":false}}}
@@ -39,8 +39,5 @@
    "env": {
      "MODE": "test"
    }
-  },
-  "required_secrets": [
-    "API_TOKEN"
-  ]
+  }
 }
@@ -1 +1 @@
-{"artifact":{"digest":"sha256:code","entrypoint":"embed","kind":"python_callable"},"created_at":"2026-08-21T00:00:00Z","environment_digest":"sha256:environment","name":"embed","required_secrets":["HF_TOKEN"],"runtime":{"env":{"TOKENIZERS_PARALLELISM":"false"},"environment":{"kind":"pip","packages":["sentence-transformers>=3"]},"kind":"python","python_version":"3.12"},"runtime_digest":"sha256:runtime","signature":{"inputs":[{"arrow_type":"utf8","name":"text","nullable":true}],"output":{"arrow_type":"list<float32>","kind":"scalar","nullable":false}},"version":"fv_01K3EXACT"}
+{"artifact":{"digest":"sha256:code","entrypoint":"embed","kind":"python_callable"},"created_at":"2026-08-21T00:00:00Z","environment_digest":"sha256:environment","name":"embed","runtime":{"env":{"TOKENIZERS_PARALLELISM":"false"},"environment":{"kind":"pip","packages":["sentence-transformers>=3"]},"kind":"python","python_version":"3.12"},"runtime_digest":"sha256:runtime","signature":{"inputs":[{"arrow_type":"utf8","name":"text","nullable":true}],"output":{"arrow_type":"list<float32>","kind":"scalar","nullable":false}},"version":"fv_01K3EXACT"}
@@ -17,7 +17,6 @@
          {"name": "token_count", "arrow_type": "int64", "nullable": false}
        ]
      },
-      "group_id": "fg_01K3TEXT",
      "columns": {"normalized_text": "search_text"}
    },
    "binding_metadata_version": 1,
@@ -3,7 +3,7 @@
  "job_type": "refresh_function_columns",
  "job_state": "DONE",
  "creation_ms": 1787270400001,
-  "spec": {"table": "documents", "binding_revision": 3},
+  "spec": {"table": "documents", "binding_id": "fb_01K3TEXT"},
  "result": {
    "rows_assigned": 999998800,
    "rows_failed": 0,
--- a/Show More
+++ b/Show More