chore(release): v2.5.0

Bump all workspace crates, the node package and pyproject to 2.5.0, fold the
two unreleased sections together and add upgrade notes for the behaviour
changes.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>

CHANGELOG.md

@@ -1,6 +1,27 @@
 # Changelog
 
-## Unreleased
+## v2.5.0 (2026-09-19)
+
+### Upgrade Notes
+- **Retrieval rankings change, for the better.** The default fusion weights
+  move from `0.7/0.3` to `0.4/0.6` (`hybrid::DEFAULT_FUSION`), measured over the
+  full LongMemEval haystack: turn-level Hit@1 51.6% vs 44.2%, MRR 0.643 vs
+  0.586. `unified_search` and the OpenClaw backend pick this up automatically;
+  callers passing weights to `hybrid_search` explicitly are unaffected.
+- **Out-of-range selections are now errors.** `read_*_selection` used to return
+  data for a selection that ran past a dataset edge — a hyperslab came back
+  zero-padded, and a point with an out-of-range coordinate wrapped into the
+  next row. Both are now `FormatError::SelectionOutOfBounds`. Code relying on
+  the old (wrong) values will start seeing errors.
+- **Large compressed datasets written without explicit chunk dimensions get a
+  different layout.** They used to be stored as one chunk; they are now split
+  to ~1 MiB chunks. The files stay standard and h5py-readable, and explicit
+  `with_chunks` is unaffected.
+- `rayon` is now a default dependency of `clawhdf5-agent` (the parallel index
+  build). Opt out with `--no-default-features --features float16,hnsw`.
+- `clawhdf5-ann` search results no longer shrink when records near the query
+  have been deleted, so a search that previously returned fewer than `k`
+  results now returns `k`.
 
 ### Retrieval quality
 - `clawhdf5-agent`: optional keyword stemming — `bm25::TokenFilter::Stemmed`
@@ -39,8 +60,6 @@
   never been measured against the weighted sum; the LongMemEval bench now has
   an `RRF` mode.
 
-## Unreleased
-
 ### HDF5 Read Path
 - **Selection reads cost what the selection costs.** `read_*_selection` decoded
   the *entire* dataset and then picked elements out, so a 64 x 64 window of a