BENCHMARKS.md

@@ -149,6 +149,49 @@ vectors, and recall is measured against brute-force ground truth rather than
 against the f32 index, whose own approximation errors a re-scored search is
 entitled to get right.
 
+### Search options: source filters, re-ranking, confidence
+
+Measured 2026-09-24 on tank (AMD Ryzen 7 7800X3D). `HDF5Memory::search`
+with `SearchOptions`, clustered 384-dim data, k = 10, Hebbian boosting off.
+Filters keep 50%, 10% or 1% of the store at random, or two whole clusters
+chosen *away* from each query's own — the case an ANN index handles worst,
+because nothing it finds near the query is allowed. Recall is vector-only
+against an exact scan of the allowed records; latency is full hybrid search
+(vector + BM25). 200 queries, medians of three runs (recall was identical in
+every run).
+
+```bash
+cargo run --release -p clawhdf5-bench --bin search_harness -- --options-study --full
+```
+
+| N | options | filtered recall@10 | p50 ms | p99 ms |
+|---:|---|---:|---:|---:|
+| 10 000 | no filter | 1.0000 | 0.488 | 0.518 |
+| 10 000 | random 50% | 1.0000 | 0.520 | 0.563 |
+| 10 000 | random 10% | 1.0000 | 0.342 | 0.367 |
+| 10 000 | random 1% | 1.0000 | 0.220 | 0.239 |
+| 10 000 | 2 clusters away from the query | 1.0000 | 0.252 | 0.278 |
+| 10 000 | re-rank | — | 0.562 | 0.674 |
+| 10 000 | re-rank + confidence | — | 0.554 | 0.575 |
+| 100 000 | no filter | 0.9995 | 4.600 | 5.245 |
+| 100 000 | random 50% | 1.0000 | 4.770 | 5.614 |
+| 100 000 | random 10% | 1.0000 | 3.244 | 4.008 |
+| 100 000 | random 1% | 1.0000 | 2.288 | 2.943 |
+| 100 000 | 2 clusters away from the query | 1.0000 | 2.298 | 3.048 |
+| 100 000 | re-rank | — | 4.747 | 5.411 |
+| 100 000 | re-rank + confidence | — | 4.748 | 5.526 |
+
+A filtered search finds the exact filtered top 10 and is never slower than an
+unfiltered one. The filter applies before ranking — over-fetching the index in
+proportion to what the filter removes, and scanning the allowed records
+exactly whenever that costs fewer distance evaluations than the index would
+(roughly `pool × M`). The first version compared the over-fetch to the store
+size instead, and that measured badly at 100K: the 1% filter took 5.9 ms at
+recall 0.9965 and the away-from-query filter 12.3 ms at 0.976, both through an
+index asked for ~16 000 candidates, where scanning the few hundred or thousand
+allowed records is exact and cheap. Re-ranking a 3k candidate pool and
+confidence rejection add about 3%.
+
 ### float16 embedding storage (`MemoryConfig::float16`)
 
 Measured 2026-09-23 on tank (AMD Ryzen 7 7800X3D). The same clustered
@@ -183,6 +226,30 @@ The in-memory cache holds the half-rounded values, so the store searches the
 same before and after a reopen; RAM use is unchanged (the cache is still
 `f32`). What `float16` saves is disk, and the I/O that goes with it.
 
+**On real embeddings.** The table above is synthetic clustered data. The full
+LongMemEval haystack (`longmemeval_s`, 500 questions, ~494 turns each) with
+real all-MiniLM-L6-v2 embeddings, run once with `f32` stores and once with
+`--float16`, measured 2026-09-24 on tank (embeddings on an RTX 5060 Ti):
+
+```bash
+cargo run --release -p clawhdf5-bench --bin longmemeval_bench --features embeddings-cuda -- \
+  benchmarks/longmemeval/longmemeval_s.json --embeddings weights/all-minilm-l6-v2 [--float16]
+```
+
+| mode | turn Hit@1 | turn Hit@5 | turn Hit@10 | turn MRR | session Hit@5 | session MRR |
+|---|---:|---:|---:|---:|---:|---:|
+| Hybrid 0.4 / 0.6, f32 | 51.6% | 81.4% | 87.8% | 0.6430 | 96.8% | 0.9347 |
+| Hybrid 0.4 / 0.6, float16 | 51.6% | 81.4% | 87.8% | 0.6430 | 96.8% | 0.9347 |
+| Vector only, f32 | 36.0% | 71.8% | 81.6% | 0.5031 | 94.2% | 0.8901 |
+| Vector only, float16 | 36.0% | 71.8% | 81.6% | 0.5031 | 94.2% | 0.8901 |
+
+All eight modes the harness runs (BM25, vector, hybrid, RRF, stemmed, and
+both re-rank variants) were identical at every Hit@k and MRR, turn and session
+level, except RRF's session MRR (0.9253 vs 0.9254) and one or two flips in
+which of two gold sessions ranks first, out of ~320. Those flips show the
+half-precision path was in effect; they do not change a single hit. The f32
+run reproduces the published hybrid numbers exactly.
+
 ### Opening a store (`read_from_disk`)
 
 `HDF5Memory::open` memory-mapped the file, copied the whole mapping into a

CHANGELOG.md

@@ -11,12 +11,18 @@
   each checkpoint, so it becomes readable at its next checkpoint on this
   version; other files with `f32` or empty datasets need rewriting. Details in
   `docs/known-issues.md`.
-- **`MemoryConfig::float16` now does what it says.** It was persisted and
-  otherwise ignored; embeddings were always stored as `f32`. A store created
-  with it on now writes half-precision embeddings (48% smaller files) and
-  rounds embeddings to half precision as they are saved. A store that already
-  had `float16 = true` rounds its embeddings when next opened and writes them
-  as `float16` at its next checkpoint. Off by default.
+- **New stores store embeddings as half precision by default.**
+  `MemoryConfig::float16` was persisted and otherwise ignored; it now writes
+  `float16` embeddings (48% smaller files at 100K) and rounds each embedding
+  to half precision as it is saved — and it defaults to `true` for new
+  stores. On the full LongMemEval haystack with real MiniLM embeddings every
+  retrieval metric matched `f32`. **Existing stores are unaffected**: every
+  agent store has recorded `float16 = false`, and keeps it (a v2.5.0 fixture
+  guards this). A store that already had `float16 = true` rounds its
+  embeddings when next opened and writes them as `float16` at its next
+  checkpoint. Opt out with `float16 = false` or `create --f32`; the CLI's
+  `--float16` is still accepted and now a no-op. Values beyond ±65504 are
+  refused, so keep `f32` for unnormalised vectors.
 - **Breaking:** `MemoryError` gained `InvalidEntry`, returned when a
   `float16` store is given an embedding value beyond ±65504. Exhaustive
   matches need the new arm.
@@ -40,6 +46,28 @@
   `quantized_index = false`, or pass `create --f32-index` to the CLI, to opt
   out. The CLI's `--quantized-index` is still accepted but is now a no-op.
 
+### Search
+- `clawhdf5-agent`: **`HDF5Memory::search` with `SearchOptions`** — source
+  filtering, re-ranking and confidence rejection in the store's own search
+  path. Re-ranking and confidence rejection used to be reachable only
+  through the OpenClaw backend, which now calls `search` with both on.
+  - `with_sources([..])` restricts a search to records from those source
+    channels. It applies before ranking, so a filtered search still returns up
+    to `k` results, normalised over what it can return. Measured at 100K: the
+    exact filtered top 10 for filters keeping 50%, 10% and 1% of the store and
+    for records far from the query, and never slower than an unfiltered search
+    (2.3 ms for a 1% filter vs 4.6 ms unfiltered). See `BENCHMARKS.md`,
+    "Search options".
+  - `with_rerank(ReRankConfig)` re-ranks a pool of `max(3k, 10)` candidates
+    (`rerank_pool` to change it) by relevance, recency, source authority and
+    activation; `with_confidence(ConfidenceConfig)` drops low-confidence
+    results; `at_time(now)` pins the clock for recency. About 3% on latency.
+  - `hybrid_search` and `hybrid_search_with` are unchanged (tested bit for
+    bit against `search` with default options).
+- `clawhdf5-agent`: the OpenClaw backend's search now boosts the Hebbian
+  activation of the `k` results it returns, not of the whole `3k` candidate
+  pool it re-ranks.
+
 ### Interop
 - `clawhdf5-format`: **every `f32` dataset was unreadable by h5py and
   libhdf5.** The float datatype encoder hard-coded the sign bit's position to
@@ -67,7 +95,9 @@
   precision.** At 100K x 384 the file goes from 154.0 to 80.8 MiB (−48%), a
   checkpoint from 752 to 512 ms and open from 300 to 252 ms, with the same
   vector recall@10 against an exact scan (0.999 vs 0.994) and the same
-  `hybrid_search` latency; at 10K open is 3 ms slower. The cache rounds each
+  `hybrid_search` latency; at 10K open is 3 ms slower. On the full
+  LongMemEval haystack with real MiniLM embeddings every retrieval metric is
+  identical to `f32` (`longmemeval_bench --float16`). The cache rounds each
   embedding as it is saved, so memory and file agree bit for bit and a store
   returns the same results before and after a reopen (tested). Out-of-range
   values are refused with `MemoryError::InvalidEntry` rather than stored as
@@ -99,6 +129,11 @@
   now an error.
 
 ### Defaults
+- `clawhdf5-agent`: `MemoryConfig::float16` defaults to `true` for new stores,
+  measured rather than assumed: identical LongMemEval retrieval on real
+  embeddings, 48% smaller files and faster checkpoints and opens at 100K.
+  `clawhdf5-cli create --f32` opts out; like `--f32-index`, it only ever
+  switches the default off.
 - `clawhdf5-agent`: `MemoryConfig::quantized_index` defaults to `true` for new
   stores. The reason it had been off — that int8 search was slower on ARM —
   did not survive measurement (see Corrections). Stores that predate the

CLAUDE.md

@@ -87,16 +87,26 @@ Cargo workspace with 16 crates under `crates/` (plus `libaec-sys`, an internal F
   `export` do). An unreadable WAL (torn header, bad magic) is quarantined to
   `<store>.h5.wal.corrupt-<ts>` rather than blocking `open()`; a WAL with an
   unknown *newer* version still fails and is left untouched.
-- `MemoryConfig::float16` (off by default, persisted; CLI `create --float16`)
-  writes `/memory/embeddings` as IEEE half precision (48% smaller file at
-  100K, same recall). `MemoryCache::half_precision` rounds each embedding as
-  it enters the cache (push, update, WAL replay, and on load of a store still
+- `MemoryConfig::float16` (**on by default** for new stores, persisted;
+  existing stores keep their recorded `false` — guarded by the v2.5.0
+  fixture in `tests/float16_store.rs`; CLI opt-out is `create --f32`) writes
+  `/memory/embeddings` as IEEE half precision (48% smaller file at 100K;
+  LongMemEval with real MiniLM embeddings identical to f32).
+  `MemoryCache::half_precision` rounds each embedding as it enters the cache (push, update, WAL replay, and on load of a store still
   `f32` on disk), so memory and file agree bit for bit; the conversions live
   in `clawhdf5_format::float16` and must stay the single implementation.
   Values beyond ±65504 are `MemoryError::InvalidEntry`. Interop: every file
   must open in h5py — `f32` datasets and empty datasets did not until
   2026-09-23 (see `docs/known-issues.md`); the agent's `h5py_interop` test
   guards a whole store.
+- `HDF5Memory::search(query_emb, text, &SearchOptions)` is the full search
+  path: optional source-channel filter (applied before ranking; exact scan of
+  the allowed records whenever cheaper than `pool × M` index distance
+  evaluations, and as the fallback when the pool comes back short), fusion,
+  activation scaling, optional re-ranking and confidence rejection.
+  `hybrid_search`/`hybrid_search_with` are thin wrappers; the OpenClaw
+  backend is `search` with re-rank + confidence on. Measure changes with
+  `search_harness --options-study`.
 - `MemoryConfig::compression` is off by default; when on, embeddings are
   deflate-compressed, or Zstd with the agent's `zstd` feature (links libzstd).
 - `Dataset::verify_provenance()` (clawhdf5 facade, `provenance` feature, on by

README.md

@@ -82,14 +82,18 @@ breaking change, are in [CHANGELOG.md](CHANGELOG.md).
   100K × 384 store to 1.74× the raw vectors. At equal recall it is also faster
   than `f32`: 1.63× QPS on AVX2, 1.18× on a Raspberry Pi 5 (NEON `SDOT`).
 
-**Interop (unreleased)**
+**Interop and search (unreleased)**
 - **Files we write now open in h5py and libhdf5.** Every `f32` dataset —
   including every agent store's embeddings — and every empty dataset was
   refused by libhdf5. Both were write-side bugs in every release; agent stores
   fix themselves at their next checkpoint. See
   [docs/known-issues.md](docs/known-issues.md).
 - `MemoryConfig::float16` now stores half-precision embeddings (it was
-  ignored): 48% smaller files at the same recall.
+  ignored), and is on by default for new stores: 48% smaller files, and
+  identical LongMemEval retrieval on real embeddings.
+- `HDF5Memory::search` with `SearchOptions`: filter by source channel (exact
+  filtered top-k, never slower than unfiltered), and opt-in re-ranking and
+  confidence rejection, which used to be OpenClaw-only.
 
 **Tooling**
 - CI now runs the h5py/netCDF4 interop suites for real (they had been skipping
@@ -253,8 +257,9 @@ retrieval recall reported as QA accuracy typically overstates by 20–30 points.
 | 10K | 17.0 MB | 1.7 KB | 2.7 MB (6.2x) |
 | 100K | 169.8 MB | 1.7 KB | 26.9 MB (6.2x) |
 
-With `MemoryConfig::float16` the embeddings take half the space: an agent
-store of 100K × 384 records is 80.8 MiB instead of 154.0.
+These figures are `f32` embeddings. New agent stores default to
+`MemoryConfig::float16`, which halves them: 100K × 384 records take 80.8 MiB
+instead of 154.0.
 
 **In memory** — a store reopened from disk, 384-dim `f32`, measured with a
 counting allocator ([BENCHMARKS.md § Memory footprint](BENCHMARKS.md#memory-footprint)):
@@ -293,12 +298,14 @@ ClawhDF5's agent memory engine draws on 15+ recent papers on agentic memory syst
                          └────────┬────────┘
                                   │
                 ┌─────────────────▼──────────────────┐
-                │ HDF5Memory::hybrid_search          │
+                │ HDF5Memory::search                 │
+                │ optional source-channel filter     │
                 │ HNSW vector + BM25 keyword         │
                 │ weighted fusion (0.4 / 0.6)        │
                 │ × √(Hebbian activation)            │
                 └─────────────────┬──────────────────┘
-                                  │  OpenClaw backend adds:
+                                  │  opt-in (SearchOptions);
+                                  │  the OpenClaw backend turns both on
                 ┌─────────────────▼──────────────────┐
                 │ Multi-factor re-ranking            │
                 │ relevance · recency · authority ·  │
@@ -334,8 +341,8 @@ directly; the store persists the records, sessions and graph they work over.
 | **`knowledge`** | Entity/relation graph with BFS traversal, spreading activation, fuzzy (Levenshtein) entity resolution |
 | **`consolidation`** | Three-tier memory (Working → Episodic → Semantic) with importance scoring, novelty, and time-decay |
 | **`hybrid`** | Vector + BM25 fusion. Default is a min-max-normalised weighted sum, vector 0.4 / keyword 0.6 (`hybrid::DEFAULT_FUSION`, tuned on LongMemEval); RRF is available via `Fusion::Rrf` / `hybrid_search_with`. The vector stage uses the HNSW index by default (`hnsw` feature); disable with `--no-default-features --features float16` for an exact linear scan |
-| **`reranker`** | Multi-factor re-ranking: retrieval relevance (leads, weight 1.0), temporal recency, source authority, activation weight. Used by the OpenClaw backend |
-| **`confidence`** | Low-confidence rejection — suppresses spurious recalls when nothing matches (OpenClaw backend) |
+| **`reranker`** | Multi-factor re-ranking: retrieval relevance (leads, weight 1.0), temporal recency, source authority, activation weight. Opt-in via `SearchOptions::with_rerank`; on in the OpenClaw backend |
+| **`confidence`** | Low-confidence rejection — suppresses spurious recalls when nothing matches. Opt-in via `SearchOptions::with_confidence`; on in the OpenClaw backend |
 | **`temporal`** | Sorted timestamp index, session DAG, entity timeline, temporal query hints |
 | **`multimodal`** | Cross-modal search across text/image/audio/video embeddings |
 | **`provenance`** | Source attribution and an unkeyed FNV-1a content hash per record, held in memory for the session, for detecting accidental corruption (not tamper-proof) |
@@ -401,6 +408,31 @@ for result in results {
 }
 ```
 
+### Search Options
+
+```rust
+use clawhdf5_agent::SearchOptions;
+use clawhdf5_agent::confidence::ConfidenceConfig;
+use clawhdf5_agent::reranker::ReRankConfig;
+
+// Only memories from these source channels; still a full page of k results.
+let work = memory.search(
+    &query_embedding,
+    "deadline",
+    &SearchOptions::new(5).with_sources(["slack", "email"]),
+);
+
+// Re-rank by relevance, recency, source authority and activation, then drop
+// low-confidence results — the pipeline the OpenClaw backend runs.
+let careful = memory.search(
+    &query_embedding,
+    "user preferences",
+    &SearchOptions::new(5)
+        .with_rerank(ReRankConfig::default())
+        .with_confidence(ConfidenceConfig::default()),
+);
+```
+
 ### Knowledge Graph
 
 ```rust
@@ -580,12 +612,14 @@ setting existed keep their `f32` index; opt out for new stores with
 `quantized_index = false` or `clawhdf5-cli create --f32-index`. See
 [BENCHMARKS.md § Quantising the index copy](BENCHMARKS.md#quantising-the-index-copy-quantized_index).
 
-`MemoryConfig::float16` (off by default; CLI `create --float16`) stores the
+`MemoryConfig::float16` (**on by default** for new stores) stores the
 embeddings on disk as IEEE half precision (numpy `float16`): at 100K × 384 the
-file drops from 154 to 81 MiB, checkpoints and opens get faster, and vector
-recall and search latency do not change. Embeddings are rounded as they are
-saved, so the store searches the same before and after a reopen; values must
-lie within ±65504. See
+file drops from 154 to 81 MiB, checkpoints and opens get faster, and on the
+full LongMemEval haystack with real MiniLM embeddings every retrieval metric
+matches `f32`. Embeddings are rounded as they are saved, so the store searches
+the same before and after a reopen; values must lie within ±65504. Existing
+stores keep their setting. Opt out with `float16 = false` or
+`clawhdf5-cli create --f32` — e.g. for unnormalised vectors. See
 [BENCHMARKS.md § float16 embedding storage](BENCHMARKS.md#float16-embedding-storage-memoryconfigfloat16).
 
 ### `clawhdf5-format`

crates/clawhdf5-agent/src/hybrid.rs

@@ -65,31 +65,34 @@ pub fn hybrid_search_fused(
 ) -> Vec<(usize, f32)> {
     // Get raw scores from both systems. Request all results so normalization
     // covers the full distribution.
-    // Use parallel search when rayon feature is enabled and vector count > 10K.
-    let vec_scores = {
-        #[cfg(feature = "parallel")]
-        {
-            if vectors.count() > 10_000 {
-                vector_search::parallel_cosine_batch(
-                    query_embedding,
-                    vectors,
-                    tombstones,
-                    vectors.count(),
-                )
-            } else {
-                vector_search::cosine_similarity_batch(query_embedding, vectors, tombstones)
-            }
-        }
-        #[cfg(not(feature = "parallel"))]
-        {
-            vector_search::cosine_similarity_batch(query_embedding, vectors, tombstones)
-        }
-    };
+    let vec_scores = exact_vector_scores(query_embedding, vectors, tombstones);
     let kw_scores = bm25_index.scores(query_text);
 
     fuse(vec_scores, kw_scores, fusion, k)
 }
 
+/// Cosine similarity of `query_embedding` to every vector whose `skip` byte is
+/// 0 (a tombstone, or any other exclusion mask). Parallel above 10K vectors
+/// when the `parallel` feature is on.
+pub fn exact_vector_scores(
+    query_embedding: &[f32],
+    vectors: &(impl crate::vector_search::VectorSet + Sync + ?Sized),
+    skip: &[u8],
+) -> Vec<(usize, f32)> {
+    #[cfg(feature = "parallel")]
+    {
+        if vectors.count() > 10_000 {
+            return vector_search::parallel_cosine_batch(
+                query_embedding,
+                vectors,
+                skip,
+                vectors.count(),
+            );
+        }
+    }
+    vector_search::cosine_similarity_batch(query_embedding, vectors, skip)
+}
+
 /// Merge pre-computed vector-similarity and keyword scores into a single ranking.
 ///
 /// Both score sets are independently min-max normalized to [0, 1] and combined

crates/clawhdf5-agent/src/lib.rs

@@ -72,6 +72,7 @@ use ephemeral::{EphemeralConfig, EphemeralStore};
 pub use ephemeral::{EphemeralEntry, EphemeralStats};
 use knowledge::KnowledgeCache;
 use memory_strategy::{Exchange, MemoryStrategy, StrategyOutput};
+pub use search::SearchOptions;
 use session::SessionCache;
 
 // --- Error type ---
@@ -135,6 +136,13 @@ pub struct MemoryConfig {
     /// so search results are the same before and after a reopen. Values must
     /// lie within ±65504; a save outside that is `MemoryError::InvalidEntry`.
     /// Fixed when the store is created (persisted in `/meta`).
+    ///
+    /// **On by default for new stores**: on the full LongMemEval haystack with
+    /// real MiniLM embeddings every retrieval metric matched `f32`, and at
+    /// 100K records the file is 48% smaller (`BENCHMARKS.md`). Existing
+    /// stores keep the setting they were created with. Set it to `false` for
+    /// full-precision embeddings, e.g. for unnormalised vectors that may
+    /// exceed the half-precision range.
     pub float16: bool,
     pub compression: bool,
     pub compression_level: u32,
@@ -190,7 +198,7 @@ impl MemoryConfig {
             embedding_dim,
             chunk_size: 512,
             overlap: 50,
-            float16: false,
+            float16: true,
             compression: false,
             compression_level: 0,
             compact_threshold: 0.3,

crates/clawhdf5-agent/src/openclaw.rs

@@ -13,9 +13,8 @@ use std::path::{Path, PathBuf};
 use std::time::{SystemTime, UNIX_EPOCH};
 
 use crate::{
-    AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry,
-    confidence::{ConfidenceConfig, ScoredResult, reject_low_confidence},
-    reranker::{ReRankConfig, RerankInput, rerank},
+    AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry, SearchOptions,
+    confidence::ConfidenceConfig, reranker::ReRankConfig,
 };
 
 // ─────────────────────────────────────────────────────────────────────────────
@@ -524,71 +523,27 @@ impl ClawhdfBackend {
 
 impl MemoryBackend for ClawhdfBackend {
     /// Search using hybrid vector + BM25 retrieval, then re-rank and
-    /// confidence-filter.
+    /// confidence-filter — [`HDF5Memory::search`] with both stages on.
     fn search(
         &mut self,
         query_text: &str,
         query_embedding: &[f32],
         k: usize,
     ) -> Vec<MemorySearchResult> {
-        // 1. Hybrid retrieval (vector + BM25, fused by score).
-        let candidates = k.saturating_mul(3).max(10);
-        let raw = self.memory.hybrid_search_with(
-            query_embedding,
-            query_text,
-            crate::hybrid::DEFAULT_FUSION,
-            candidates,
-        );
-
-        if raw.is_empty() {
-            return Vec::new();
-        }
-
-        let now = Self::now_secs();
-
-        // 2. Re-rank using temporal recency, source authority, Hebbian weight.
-        let rerank_inputs: Vec<RerankInput> = raw
-            .iter()
-            .map(|r| RerankInput {
-                index: r.index,
-                timestamp: r.timestamp,
-                source_channel: r.source_channel.clone(),
-                raw_activation: r.activation,
-                relevance: r.score,
-            })
-            .collect();
-
-        let reranked = rerank(&rerank_inputs, &self.rerank_config, now);
-
-        // 3. Confidence rejection.
-        let scored: Vec<ScoredResult> = reranked
-            .iter()
-            .map(|r| ScoredResult {
-                index: r.index,
-                score: r.combined_score,
-            })
-            .collect();
-
-        let confident = reject_low_confidence(&scored, &self.confidence_config);
-
-        // 4. Map back to MemorySearchResult; preserve raw text via index lookup.
-        let raw_by_idx: HashMap<usize, &crate::SearchResult> =
-            raw.iter().map(|r| (r.index, r)).collect();
-
-        confident
+        let options = SearchOptions::new(k)
+            .with_rerank(self.rerank_config)
+            .with_confidence(self.confidence_config.clone())
+            .at_time(Self::now_secs());
+        self.memory
+            .search(query_embedding, query_text, &options)
             .into_iter()
-            .take(k)
-            .filter_map(|sr| {
-                let r = raw_by_idx.get(&sr.index)?;
-                let path = r.source_channel.clone();
-                Some(MemorySearchResult {
-                    text: r.chunk.clone(),
-                    score: sr.score,
-                    path: path.clone(),
-                    line_range: None,
-                    timestamp: Some(r.timestamp),
-                    source: path,
-                })
+            .map(|r| MemorySearchResult {
+                text: r.chunk,
+                score: r.score,
+                path: r.source_channel.clone(),
+                line_range: None,
+                timestamp: Some(r.timestamp),
+                source: r.source_channel,
             })
             .collect()
     }

crates/clawhdf5-agent/src/search.rs

@@ -2,18 +2,107 @@
 
 use std::path::Path;
 
+use std::collections::HashSet;
+
 use crate::bm25;
+use crate::confidence::{ConfidenceConfig, ScoredResult, reject_low_confidence};
 use crate::hybrid;
+use crate::reranker::{ReRankConfig, RerankInput, rerank};
 use crate::{HDF5Memory, MAX_ACTIVATION_WEIGHT, MemoryError, Result, SearchResult};
 
+/// Options for [`HDF5Memory::search`].
+///
+/// [`SearchOptions::new`] is plain hybrid search with the tuned default
+/// fusion — the same as `hybrid_search_with(.., hybrid::DEFAULT_FUSION, k)`.
+/// Every stage beyond that is opt-in.
+#[derive(Debug, Clone)]
+pub struct SearchOptions {
+    /// Number of results to return.
+    pub k: usize,
+    /// How the vector and keyword stages are combined.
+    pub fusion: hybrid::Fusion,
+    /// Only consider records whose `source_channel` is one of these. The
+    /// filter applies *before* ranking, so a filtered search still returns up
+    /// to `k` results and scores are normalised over the records it can
+    /// return. `None` searches everything; an empty list matches nothing.
+    pub source_channels: Option<Vec<String>>,
+    /// Re-rank a candidate pool by retrieval relevance, recency, source
+    /// authority and activation — the pipeline the OpenClaw backend runs.
+    pub rerank: Option<ReRankConfig>,
+    /// Candidates retrieved for re-ranking; 0 means `max(3k, 10)`.
+    pub rerank_pool: usize,
+    /// Drop low-confidence results (after re-ranking, when that is on).
+    pub confidence: Option<ConfidenceConfig>,
+    /// The time recency is measured from, in seconds since the epoch.
+    /// `None` uses the system clock.
+    pub now: Option<f64>,
+}
+
+impl SearchOptions {
+    pub fn new(k: usize) -> Self {
+        Self {
+            k,
+            fusion: hybrid::DEFAULT_FUSION,
+            source_channels: None,
+            rerank: None,
+            rerank_pool: 0,
+            confidence: None,
+            now: None,
+        }
+    }
+
+    pub fn with_fusion(mut self, fusion: hybrid::Fusion) -> Self {
+        self.fusion = fusion;
+        self
+    }
+
+    /// Search only records from these source channels.
+    pub fn with_sources<S: Into<String>>(mut self, channels: impl IntoIterator<Item = S>) -> Self {
+        self.source_channels = Some(channels.into_iter().map(Into::into).collect());
+        self
+    }
+
+    pub fn with_rerank(mut self, config: ReRankConfig) -> Self {
+        self.rerank = Some(config);
+        self
+    }
+
+    pub fn with_confidence(mut self, config: ConfidenceConfig) -> Self {
+        self.confidence = Some(config);
+        self
+    }
+
+    /// Measure recency from `now` (seconds since the epoch) instead of the
+    /// system clock — for reproducible results and tests.
+    pub fn at_time(mut self, now: f64) -> Self {
+        self.now = Some(now);
+        self
+    }
+}
+
+impl Default for SearchOptions {
+    fn default() -> Self {
+        Self::new(10)
+    }
+}
+
 impl HDF5Memory {
-    /// Vector + keyword scoring stage of [`HDF5Memory::hybrid_search`].
+    /// Vector + keyword scoring stage of [`HDF5Memory::search`].
     ///
     /// Without the `hnsw` feature this is a full linear cosine scan (the exact
     /// previous behaviour, also used as the correctness oracle in tests). With
     /// `hnsw` enabled and an index available, the vector candidates come from an
     /// approximate-nearest-neighbour search over an over-fetched pool, then merge
     /// with BM25 via the shared [`hybrid::merge_vector_keyword`].
+    ///
+    /// `exclude`, when given, marks records that must not be returned (1 =
+    /// excluded; it covers tombstones too). The index is over-fetched in
+    /// proportion to how much the mask removes. Surfacing `pool` candidates
+    /// costs the index roughly `pool × M` distance evaluations, while an exact
+    /// scan of the allowed records costs one each — so whenever that scan is
+    /// the cheaper of the two it is used instead, and it is also the fallback
+    /// if the pool comes back with too few allowed hits (the allowed records
+    /// sit away from the query). A filtered search never comes back short.
     #[cfg(feature = "hnsw")]
     fn vector_keyword_search(
         &mut self,
@@ -22,16 +111,30 @@ impl HDF5Memory {
         bm25: &bm25::BM25Index,
         fusion: hybrid::Fusion,
         k: usize,
+        exclude: Option<&[u8]>,
     ) -> Vec<(usize, f32)> {
         self.ensure_hnsw_fresh();
+        let n = self.cache.len();
+        // Over-fetch so the merge sees a useful vector pool. `ef` is
+        // configurable, but the pool the fusion stage sees is not tied to it:
+        // a caller lowering `ef` for speed should not silently narrow what
+        // fusion has to work with.
+        let mut pool = (k * 8).max(64);
+        let mut allowed = n;
+        if let Some(ex) = exclude {
+            allowed = ex.iter().filter(|&&e| e == 0).count();
+            if allowed == 0 {
+                return Vec::new();
+            }
+            // Expect `pool` allowed hits if the filter is independent of the
+            // query's neighbourhood.
+            pool = pool.saturating_mul(n).div_ceil(allowed);
+            if allowed <= pool.saturating_mul(self.hnsw_m()) {
+                return self.exact_masked_search(query_embedding, query_text, bm25, fusion, k, ex);
+            }
+        }
         match self.hnsw.as_ref() {
             Some(index) if !index.is_empty() && index.dimension() == query_embedding.len() => {
-                // Over-fetch so the merge sees a useful vector pool; cosine
-                // distance from the index converts back to similarity (1 - d).
-                // `ef` is configurable, but the pool the fusion stage sees is
-                // not tied to it: a caller lowering `ef` for speed should not
-                // silently narrow what fusion has to work with.
-                let pool = (k * 8).max(64);
                 let ef = self.hnsw_ef_search(k).max(pool);
                 let candidates = index.search(query_embedding, pool, ef);
                 // A quantised index returns approximate distances, and no
@@ -42,6 +145,7 @@ impl HDF5Memory {
                 let exact = index.storage() == clawhdf5_ann::Storage::Int8;
                 let vec_scores: Vec<(usize, f32)> = candidates
                     .into_iter()
+                    .filter(|(id, _)| exclude.is_none_or(|ex| ex[*id] == 0))
                     .map(|(id, dist)| {
                         let score = if exact {
                             crate::vector_search::cosine_similarity(
@@ -56,10 +160,54 @@ impl HDF5Memory {
                     .collect();
                 // Fusion normalises over every keyword match, so it needs all
                 // the scores — but not ranked.
-                let kw_scores = bm25.scores(query_text);
+                let mut kw_scores = bm25.scores(query_text);
+                if let Some(ex) = exclude {
+                    if vec_scores.len() < k.min(allowed) {
+                        // The allowed records are not where the index looked.
+                        return self.exact_masked_search(
+                            query_embedding,
+                            query_text,
+                            bm25,
+                            fusion,
+                            k,
+                            ex,
+                        );
+                    }
+                    kw_scores.retain(|(id, _)| ex[*id] == 0);
+                }
                 hybrid::fuse(vec_scores, kw_scores, fusion, k)
             }
-            _ => hybrid::hybrid_search_fused(
+            _ => match exclude {
+                Some(ex) => {
+                    self.exact_masked_search(query_embedding, query_text, bm25, fusion, k, ex)
+                }
+                None => hybrid::hybrid_search_fused(
+                    query_embedding,
+                    query_text,
+                    &self.cache.embeddings,
+                    &self.cache.chunks,
+                    &self.cache.tombstones,
+                    bm25,
+                    fusion,
+                    k,
+                ),
+            },
+        }
+    }
+
+    #[cfg(not(feature = "hnsw"))]
+    fn vector_keyword_search(
+        &mut self,
+        query_embedding: &[f32],
+        query_text: &str,
+        bm25: &bm25::BM25Index,
+        fusion: hybrid::Fusion,
+        k: usize,
+        exclude: Option<&[u8]>,
+    ) -> Vec<(usize, f32)> {
+        match exclude {
+            Some(ex) => self.exact_masked_search(query_embedding, query_text, bm25, fusion, k, ex),
+            None => hybrid::hybrid_search_fused(
                 query_embedding,
                 query_text,
                 &self.cache.embeddings,
@@ -72,25 +220,33 @@ impl HDF5Memory {
         }
     }
 
-    #[cfg(not(feature = "hnsw"))]
-    fn vector_keyword_search(
-        &mut self,
+    /// Exact hybrid search over the records `exclude` leaves (0 = allowed).
+    fn exact_masked_search(
+        &self,
         query_embedding: &[f32],
         query_text: &str,
         bm25: &bm25::BM25Index,
         fusion: hybrid::Fusion,
         k: usize,
+        exclude: &[u8],
     ) -> Vec<(usize, f32)> {
-        hybrid::hybrid_search_fused(
-            query_embedding,
-            query_text,
-            &self.cache.embeddings,
-            &self.cache.chunks,
-            &self.cache.tombstones,
-            bm25,
-            fusion,
-            k,
-        )
+        let vec_scores =
+            hybrid::exact_vector_scores(query_embedding, &self.cache.embeddings, exclude);
+        let mut kw_scores = bm25.scores(query_text);
+        kw_scores.retain(|(id, _)| exclude.get(*id) == Some(&0));
+        hybrid::fuse(vec_scores, kw_scores, fusion, k)
+    }
+
+    /// The exclusion mask for a source-channel filter: 1 for a tombstoned
+    /// record or one from a channel not in `channels`.
+    fn source_mask(&self, channels: &[String]) -> Vec<u8> {
+        let allowed: HashSet<&str> = channels.iter().map(String::as_str).collect();
+        self.cache
+            .source_channels
+            .iter()
+            .zip(&self.cache.tombstones)
+            .map(|(ch, &t)| u8::from(t != 0 || !allowed.contains(ch.as_str())))
+            .collect()
     }
 
     /// Perform hybrid search combining cosine vector similarity and BM25 keyword search.
@@ -125,12 +281,53 @@ impl HDF5Memory {
         fusion: hybrid::Fusion,
         k: usize,
     ) -> Vec<SearchResult> {
+        self.search(
+            query_embedding,
+            query_text,
+            &SearchOptions::new(k).with_fusion(fusion),
+        )
+    }
+
+    /// Hybrid search with optional source filtering, re-ranking and
+    /// confidence rejection — see [`SearchOptions`].
+    ///
+    /// Stages, in order: vector + keyword retrieval over the records the
+    /// source filter allows; fusion; scaling by Hebbian activation; re-ranking
+    /// (if on) of a `rerank_pool` of candidates; confidence rejection (if on);
+    /// the top `k`. The records returned with a positive score get their
+    /// Hebbian boost.
+    pub fn search(
+        &mut self,
+        query_embedding: &[f32],
+        query_text: &str,
+        options: &SearchOptions,
+    ) -> Vec<SearchResult> {
+        let k = options.k;
+        let fetch = match options.rerank {
+            Some(_) if options.rerank_pool > 0 => options.rerank_pool.max(k),
+            Some(_) => k.saturating_mul(3).max(10),
+            None => k,
+        };
+        let exclude = options
+            .source_channels
+            .as_deref()
+            .map(|channels| self.source_mask(channels));
+
         // The keyword index lives for the life of the store and is updated
         // incrementally. Take it out for the duration of the call so the
         // vector stage can borrow `self` mutably, then put it back.
         self.ensure_bm25_fresh();
         let bm25 = self.bm25.take().expect("ensure_bm25_fresh leaves an index");
-        let scored = self.vector_keyword_search(query_embedding, query_text, &bm25, fusion, k);
+        let scored = self.vector_keyword_search(
+            query_embedding,
+            query_text,
+            &bm25,
+            options.fusion,
+            fetch,
+            exclude.as_deref(),
+        );
+        self.bm25 = Some(bm25);
+
         let mut results: Vec<SearchResult> = scored
             .into_iter()
             .map(|(idx, score)| {
@@ -154,6 +351,25 @@ impl HDF5Memory {
                 .then(a.index.cmp(&b.index))
         });
 
+        if let Some(config) = &options.rerank {
+            results = Self::rerank_results(results, config, options.now);
+        }
+        if let Some(config) = &options.confidence {
+            let scored: Vec<ScoredResult> = results
+                .iter()
+                .map(|r| ScoredResult {
+                    index: r.index,
+                    score: r.score,
+                })
+                .collect();
+            let keep: HashSet<usize> = reject_low_confidence(&scored, config)
+                .into_iter()
+                .map(|r| r.index)
+                .collect();
+            results.retain(|r| keep.contains(&r.index));
+        }
+        results.truncate(k);
+
         // Only reinforce records that actually matched. When fewer than `k`
         // records are relevant, the rest of the list is zero-score filler;
         // boosting it would teach the store that arbitrary records are
@@ -164,11 +380,45 @@ impl HDF5Memory {
             .map(|r| r.index)
             .collect();
         self.apply_hebbian_boost(&hit_indices);
-        self.bm25 = Some(bm25);
 
         results
     }
 
+    /// Reorder by the re-ranker's combined score, which also becomes each
+    /// result's `score`.
+    fn rerank_results(
+        results: Vec<SearchResult>,
+        config: &ReRankConfig,
+        now: Option<f64>,
+    ) -> Vec<SearchResult> {
+        let now = now.unwrap_or_else(|| {
+            std::time::SystemTime::now()
+                .duration_since(std::time::UNIX_EPOCH)
+                .map(|d| d.as_secs_f64())
+                .unwrap_or(0.0)
+        });
+        let inputs: Vec<RerankInput> = results
+            .iter()
+            .map(|r| RerankInput {
+                index: r.index,
+                timestamp: r.timestamp,
+                source_channel: r.source_channel.clone(),
+                raw_activation: r.activation,
+                relevance: r.score,
+            })
+            .collect();
+        let mut by_index: std::collections::HashMap<usize, SearchResult> =
+            results.into_iter().map(|r| (r.index, r)).collect();
+        rerank(&inputs, config, now)
+            .into_iter()
+            .filter_map(|rr| {
+                let mut r = by_index.remove(&rr.index)?;
+                r.score = rr.combined_score;
+                Some(r)
+            })
+            .collect()
+    }
+
     /// Reinforce the records a query returned. The new weights are persisted by
     /// the next checkpoint (any write that flushes, `flush_wal`, or drop) — not
     /// by rewriting the whole store inside the query, which is what made

crates/clawhdf5-agent/tests/float16_store.rs

@@ -206,3 +206,55 @@ fn wal_replay_rounds_like_a_live_save() {
     assert_eq!(recovered.count(), 30);
     assert_eq!(search_bits(&mut recovered, 77), live);
 }
+
+#[test]
+fn new_stores_default_to_float16() {
+    let dir = TempDir::new().unwrap();
+    let path = dir.path().join("default.h5");
+    let mut m = HDF5Memory::create(MemoryConfig::new(path.clone(), "agent", DIM)).unwrap();
+    assert!(m.config().float16);
+    m.save_batch((0..10).map(entry).collect()).unwrap();
+    drop(m);
+    assert_eq!(embeddings_dtype_and_values(&path).0, "Other(\"float16\")");
+    assert!(HDF5Memory::open(&path).unwrap().config().float16);
+}
+
+#[test]
+fn an_existing_f32_store_stays_f32() {
+    // Written by the v2.5.0 CLI, with `float16 = 0` in /meta (every agent
+    // store has recorded it). Flipping the default for new stores must not
+    // reach back and round an existing store's embeddings.
+    let dir = TempDir::new().unwrap();
+    let path = dir.path().join("legacy.h5");
+    std::fs::copy(
+        concat!(
+            env!("CARGO_MANIFEST_DIR"),
+            "/tests/fixtures/store_v2_5_0.h5"
+        ),
+        &path,
+    )
+    .unwrap();
+    let before = embeddings_dtype_and_values(&path);
+    assert_eq!(before.0, "F32");
+
+    let mut m = HDF5Memory::open(&path).unwrap();
+    assert!(!m.config().float16, "an old store must reopen as f32");
+    let dim = m.config().embedding_dim;
+    let odd: Vec<f32> = (0..dim).map(|i| 0.1 + i as f32 * 1e-4).collect();
+    m.save_batch(vec![MemoryEntry {
+        chunk: "added after the upgrade".into(),
+        embedding: odd.clone(),
+        source_channel: "test".into(),
+        timestamp: 1.0,
+        session_id: "s".into(),
+        tags: String::new(),
+    }])
+    .unwrap();
+    drop(m);
+
+    // Checkpointed: still f32, the old rows untouched and the new one exact.
+    let (dtype, values) = embeddings_dtype_and_values(&path);
+    assert_eq!(dtype, "F32");
+    assert_eq!(&values[..before.1.len()], before.1.as_slice());
+    assert_eq!(&values[before.1.len()..], odd.as_slice());
+}

crates/clawhdf5-agent/tests/search_options.rs

@@ -0,0 +1,344 @@
+//! `HDF5Memory::search` with `SearchOptions`: source filtering, re-ranking and
+//! confidence rejection in the store's own search path.
+
+use std::collections::HashSet;
+
+use clawhdf5_agent::confidence::ConfidenceConfig;
+use clawhdf5_agent::reranker::ReRankConfig;
+use clawhdf5_agent::{AgentMemory, HDF5Memory, MemoryConfig, MemoryEntry, SearchOptions, hybrid};
+use tempfile::TempDir;
+
+const DIM: usize = 32;
+const N: usize = 3000;
+const CLUSTERS: usize = 20;
+
+struct Rng(u64);
+impl Rng {
+    fn next(&mut self) -> u64 {
+        self.0 = self.0.wrapping_add(0x9E37_79B9_7F4A_7C15);
+        let mut z = self.0;
+        z = (z ^ (z >> 30)).wrapping_mul(0xBF58_476D_1CE4_E5B9);
+        z = (z ^ (z >> 27)).wrapping_mul(0x94D0_49BB_1331_11EB);
+        z ^ (z >> 31)
+    }
+    fn unit(&mut self) -> f32 {
+        (self.next() >> 40) as f32 / (1u64 << 24) as f32 - 0.5
+    }
+}
+
+fn normalize(v: &mut [f32]) {
+    let n = v.iter().map(|x| x * x).sum::<f32>().sqrt();
+    v.iter_mut().for_each(|x| *x /= n);
+}
+
+struct Data {
+    vectors: Vec<Vec<f32>>,
+    cluster: Vec<usize>,
+    centres: Vec<Vec<f32>>,
+}
+
+fn data() -> Data {
+    let mut rng = Rng(42);
+    let centres: Vec<Vec<f32>> = (0..CLUSTERS)
+        .map(|_| {
+            let mut c: Vec<f32> = (0..DIM).map(|_| rng.unit()).collect();
+            normalize(&mut c);
+            c
+        })
+        .collect();
+    let mut vectors = Vec::new();
+    let mut cluster = Vec::new();
+    for i in 0..N {
+        let c = i % CLUSTERS;
+        let mut v: Vec<f32> = centres[c].iter().map(|x| x + rng.unit() * 0.3).collect();
+        normalize(&mut v);
+        vectors.push(v);
+        cluster.push(c);
+    }
+    Data {
+        vectors,
+        cluster,
+        centres,
+    }
+}
+
+/// Channel of record `i` for a filter keeping `percent`% of the store at
+/// random (independent of the vectors).
+fn random_channel(i: usize, rng_seed: u64, percent: u64) -> String {
+    let mut r = Rng(rng_seed ^ (i as u64 * 7919));
+    if r.next() % 100 < percent {
+        "keep".into()
+    } else {
+        "other".into()
+    }
+}
+
+fn build(data: &Data, channel: impl Fn(usize) -> String) -> (TempDir, HDF5Memory) {
+    let dir = TempDir::new().unwrap();
+    let mut cfg = MemoryConfig::new(dir.path().join("s.h5"), "agent", DIM);
+    cfg.hebbian_boost = 0.0; // every query sees the same store
+    let mut m = HDF5Memory::create(cfg).unwrap();
+    let entries = data
+        .vectors
+        .iter()
+        .enumerate()
+        .map(|(i, v)| MemoryEntry {
+            chunk: format!("record {i} cluster {}", data.cluster[i]),
+            embedding: v.clone(),
+            source_channel: channel(i),
+            timestamp: i as f64,
+            session_id: "s".into(),
+            tags: format!("t{i}"),
+        })
+        .collect();
+    m.save_batch(entries).unwrap();
+    (dir, m)
+}
+
+/// Exact top-k by cosine among the records `allowed` keeps.
+fn exact_top(data: &Data, q: &[f32], k: usize, allowed: impl Fn(usize) -> bool) -> Vec<usize> {
+    let mut s: Vec<(usize, f32)> = (0..N)
+        .filter(|&i| allowed(i))
+        .map(|i| (i, data.vectors[i].iter().zip(q).map(|(a, b)| a * b).sum()))
+        .collect();
+    s.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0)));
+    s.into_iter().take(k).map(|(i, _)| i).collect()
+}
+
+fn query(data: &Data, i: usize) -> Vec<f32> {
+    let mut rng = Rng(1000 + i as u64);
+    let mut q: Vec<f32> = data.centres[i % CLUSTERS]
+        .iter()
+        .map(|x| x + rng.unit() * 0.3)
+        .collect();
+    normalize(&mut q);
+    q
+}
+
+fn vector_only(k: usize) -> SearchOptions {
+    SearchOptions::new(k).with_fusion(hybrid::Fusion::Weighted {
+        vector: 1.0,
+        keyword: 0.0,
+    })
+}
+
+#[test]
+fn source_filter_returns_only_allowed_records_and_a_full_page() {
+    let d = data();
+    // At N = 3000 and k = 10 the index serves a filter only when that is
+    // cheaper than scanning the allowed records: pool = 80 * N / allowed
+    // candidates at ~M = 16 distances each, against `allowed` distances. So
+    // 90% goes through the index, 50% and 1% to the exact scan.
+    for percent in [90, 50, 1] {
+        let (_dir, mut m) = build(&d, |i| random_channel(i, 5, percent));
+        let allowed = |i: usize| random_channel(i, 5, percent) == "keep";
+        let mut hits = 0;
+        for qi in 0..40 {
+            let q = query(&d, qi);
+            let got = m.search(&q, "", &vector_only(10).with_sources(["keep"]));
+            assert_eq!(got.len(), 10, "{percent}%: short page");
+            assert!(got.iter().all(|r| r.source_channel == "keep"));
+            let want: HashSet<usize> = exact_top(&d, &q, 10, allowed).into_iter().collect();
+            hits += got.iter().filter(|r| want.contains(&r.index)).count();
+        }
+        let recall = hits as f64 / 400.0;
+        let floor = if percent == 90 { 0.95 } else { 1.0 };
+        assert!(recall >= floor, "{percent}%: recall@10 {recall}");
+    }
+}
+
+#[test]
+fn filter_away_from_the_query_falls_back_to_an_exact_scan() {
+    // Channel = cluster, and the filter keeps two clusters (10% of the
+    // store) that are not the query's: the index's neighbourhood of the
+    // query holds none of them. The search must still return the exact
+    // top 10 among the allowed records, not a short or empty page.
+    let d = data();
+    let (_dir, mut m) = build(&d, |i| format!("c{}", d.cluster[i]));
+    for qi in 0..20 {
+        let q = query(&d, qi);
+        let a = format!("c{}", (qi + 7) % CLUSTERS);
+        let b = format!("c{}", (qi + 13) % CLUSTERS);
+        let got: Vec<usize> = m
+            .search(
+                &q,
+                "",
+                &vector_only(10).with_sources([a.clone(), b.clone()]),
+            )
+            .iter()
+            .map(|r| r.index)
+            .collect();
+        let want = exact_top(&d, &q, 10, |i| {
+            let c = format!("c{}", d.cluster[i]);
+            c == a || c == b
+        });
+        assert_eq!(got, want, "query {qi}");
+    }
+}
+
+#[test]
+fn filter_edge_cases() {
+    let d = data();
+    let (_dir, mut m) = build(&d, |i| random_channel(i, 9, 50));
+    let q = query(&d, 0);
+    assert!(
+        m.search(
+            &q,
+            "cluster",
+            &SearchOptions::new(10).with_sources(Vec::<String>::new())
+        )
+        .is_empty()
+    );
+    assert!(
+        m.search(
+            &q,
+            "cluster",
+            &SearchOptions::new(10).with_sources(["nope"])
+        )
+        .is_empty()
+    );
+    // Keyword matches from other channels are filtered too.
+    let got = m.search(
+        &q,
+        "record cluster",
+        &SearchOptions::new(50).with_sources(["keep"]),
+    );
+    assert_eq!(got.len(), 50);
+    assert!(got.iter().all(|r| r.source_channel == "keep"));
+    // Deleted records never come back, filtered or not.
+    let first = got[0].index;
+    m.delete(first).unwrap();
+    let again = m.search(
+        &q,
+        "record cluster",
+        &SearchOptions::new(50).with_sources(["keep"]),
+    );
+    assert!(again.iter().all(|r| r.index != first));
+}
+
+#[test]
+fn plain_options_equal_hybrid_search_with() {
+    // Two identical stores, so neither query sees the other's boosts.
+    let d = data();
+    let (_a, mut a) = build(&d, |i| random_channel(i, 3, 50));
+    let (_b, mut b) = build(&d, |i| random_channel(i, 3, 50));
+    for qi in 0..10 {
+        let q = query(&d, qi);
+        let x: Vec<(usize, u32)> = a
+            .search(&q, "record cluster 3", &SearchOptions::new(10))
+            .iter()
+            .map(|r| (r.index, r.score.to_bits()))
+            .collect();
+        let y: Vec<(usize, u32)> = b
+            .hybrid_search_with(&q, "record cluster 3", hybrid::DEFAULT_FUSION, 10)
+            .iter()
+            .map(|r| (r.index, r.score.to_bits()))
+            .collect();
+        assert_eq!(x, y);
+    }
+}
+
+fn small_store(entries: &[(&str, &str, f64)]) -> (TempDir, HDF5Memory) {
+    let dir = TempDir::new().unwrap();
+    let mut m = HDF5Memory::create(MemoryConfig::new(dir.path().join("r.h5"), "a", 4)).unwrap();
+    m.save_batch(
+        entries
+            .iter()
+            .map(|(chunk, channel, ts)| MemoryEntry {
+                chunk: chunk.to_string(),
+                embedding: vec![1.0, 0.0, 0.0, 0.0],
+                source_channel: channel.to_string(),
+                timestamp: *ts,
+                session_id: "s".into(),
+                tags: String::new(),
+            })
+            .collect(),
+    )
+    .unwrap();
+    (dir, m)
+}
+
+#[test]
+fn rerank_breaks_relevance_ties_by_recency() {
+    // Identical text and vectors, so retrieval ties; re-ranking must put the
+    // newer record first and report the combined score.
+    let now = 1_000_000.0;
+    let (_d, mut m) = small_store(&[
+        ("user prefers dark mode", "chat", now - 30.0 * 86_400.0),
+        ("user prefers dark mode", "chat", now - 60.0),
+    ]);
+    let q = [1.0, 0.0, 0.0, 0.0];
+    let plain = m.search(&q, "dark mode", &SearchOptions::new(2));
+    assert_eq!(plain[0].index, 0, "ties break by index without re-ranking");
+    let reranked = m.search(
+        &q,
+        "dark mode",
+        &SearchOptions::new(2)
+            .with_rerank(ReRankConfig::default())
+            .at_time(now),
+    );
+    assert_eq!(reranked[0].index, 1);
+    assert!(reranked[0].score > reranked[1].score);
+    assert_ne!(reranked[0].score.to_bits(), plain[0].score.to_bits());
+}
+
+#[test]
+fn confidence_rejects_when_nothing_is_good_enough() {
+    let (_d, mut m) = small_store(&[("alpha", "chat", 0.0), ("beta", "chat", 0.0)]);
+    let q = [1.0, 0.0, 0.0, 0.0];
+    let strict = ConfidenceConfig {
+        min_score: 10.0,
+        ..ConfidenceConfig::default()
+    };
+    assert!(
+        m.search(&q, "alpha", &SearchOptions::new(2).with_confidence(strict))
+            .is_empty()
+    );
+    let lenient = ConfidenceConfig {
+        min_score: 0.0,
+        min_gap: f32::INFINITY,
+        max_results: 1,
+    };
+    assert_eq!(
+        m.search(&q, "alpha", &SearchOptions::new(2).with_confidence(lenient))
+            .len(),
+        1
+    );
+}
+
+#[test]
+fn only_returned_results_are_reinforced() {
+    // With re-ranking, a pool of max(3k, 10) candidates is retrieved; only
+    // the k returned should gain activation.
+    let d = data();
+    let dir = TempDir::new().unwrap();
+    let path = dir.path().join("h.h5");
+    let mut m = HDF5Memory::create(MemoryConfig::new(path, "a", DIM)).unwrap();
+    m.save_batch(
+        (0..200)
+            .map(|i| MemoryEntry {
+                chunk: format!("record {i}"),
+                embedding: d.vectors[i].clone(),
+                source_channel: "chat".into(),
+                timestamp: i as f64,
+                session_id: "s".into(),
+                tags: String::new(),
+            })
+            .collect(),
+    )
+    .unwrap();
+    let q = query(&d, 0);
+    let got = m.search(
+        &q,
+        "record",
+        &SearchOptions::new(3).with_rerank(ReRankConfig::default()),
+    );
+    assert_eq!(got.len(), 3);
+    let returned: HashSet<usize> = got.iter().map(|r| r.index).collect();
+    // A second plain search reports each record's current activation.
+    let all = m.search(&q, "record", &SearchOptions::new(200));
+    for r in &all {
+        let boosted = r.activation > 1.0;
+        assert_eq!(boosted, returned.contains(&r.index), "record {}", r.index);
+    }
+}

crates/clawhdf5-bench/src/bin/longmemeval_bench.rs

@@ -64,6 +64,11 @@ use tempfile::TempDir;
 
 const EMBEDDING_DIM: usize = 384;
 
+/// `--float16`: build every per-question store with `MemoryConfig::float16`,
+/// so embeddings are rounded to half precision as they are saved — exactly
+/// what such a store searches over.
+static FLOAT16: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
+
 /// A mode's fusion, as one short string for the reports.
 fn describe(mode: Mode) -> String {
     let fusion = match mode.fusion {
@@ -431,6 +436,7 @@ fn evaluate_question(
     let mut config = MemoryConfig::new(dir.path().join("lme.h5"), "lme-bench", EMBEDDING_DIM);
     config.wal_enabled = false;
     config.compact_threshold = 0.0;
+    config.float16 = FLOAT16.load(std::sync::atomic::Ordering::Relaxed);
 
     let mut memory = HDF5Memory::create(config).expect("failed to create HDF5Memory");
     memory.set_token_filter(mode.tokens);
@@ -940,6 +946,10 @@ fn main() {
                 limit = Some(v.parse().expect("--limit must be a positive integer"));
             }
             "--sweep" => sweep = true,
+            "--float16" => {
+                FLOAT16.store(true, std::sync::atomic::Ordering::Relaxed);
+                eprintln!("Stores use MemoryConfig::float16 (half-precision embeddings)");
+            }
             "--rerank-sweep" => {
                 // Re-ranking needs the vector stage to have candidates worth
                 // reordering, so this is an embeddings-only comparison.
@@ -971,6 +981,9 @@ fn main() {
                      --rerank-sweep\n\
                               compare re-ranking off, metadata-only (the old\n\
                               behaviour) and blended at several half-lives.\n\
+                     --float16\n\
+                              build each store with MemoryConfig::float16, to\n\
+                              compare retrieval on half-precision embeddings.\n\
                      --sweep  instead of the three named modes, sweep vector_weight\n\
                               from 0.0 to 1.0 in 0.1 steps. The 0.7/0.3 default was\n\
                               never searched; this is what searches it."

crates/clawhdf5-bench/src/bin/search_harness.rs

@@ -20,6 +20,7 @@
 //! cargo run --release -p clawhdf5-bench --bin search_harness -- --json out.json
 //! cargo run --release -p clawhdf5-bench --bin search_harness -- --ann-only --uniform
 //! cargo run --release -p clawhdf5-bench --bin search_harness -- --float16-study --full
+//! cargo run --release -p clawhdf5-bench --bin search_harness -- --options-study --full
 //! ```
 
 use std::time::{Duration, Instant};
@@ -487,6 +488,177 @@ fn bench_end_to_end(n: usize, json: &mut Vec<serde_json::Value>) {
     }));
 }
 
+// ---------------------------------------------------------------------------
+// Search options study: source filters, re-ranking, confidence rejection
+// ---------------------------------------------------------------------------
+
+/// `--options-study`: what `HDF5Memory::search`'s options cost and whether a
+/// filtered search finds the right records. Filters keep 50%, 10% or 1% of
+/// the store at random, or two whole clusters away from the query (the case
+/// the index cannot serve, which falls back to an exact scan). Recall is
+/// vector-only against an exact scan of the allowed records; latency is full
+/// hybrid search. Hebbian boosting is off.
+fn options_study(n: usize) {
+    use clawhdf5_agent::SearchOptions;
+    use clawhdf5_agent::confidence::ConfidenceConfig;
+    use clawhdf5_agent::hybrid::Fusion;
+    use clawhdf5_agent::reranker::ReRankConfig;
+
+    let data = make_dataset(n, 0x0B7 ^ n as u64);
+    let n_clusters = data.cluster_of.iter().max().map_or(1, |m| m + 1);
+    let mut rng = Rng(5);
+    let bucket_of: Vec<usize> = (0..n).map(|_| rng.below(100)).collect();
+    let bucket = &bucket_of;
+    let query_texts: Vec<String> = data
+        .query_cluster
+        .iter()
+        .enumerate()
+        .map(|(i, c)| text_for(*c, i, &mut rng))
+        .collect();
+    let exact_top = |q: &[f32], allowed: &dyn Fn(usize) -> bool| -> Vec<usize> {
+        let mut s: Vec<(usize, f32)> = (0..n)
+            .filter(|&i| allowed(i))
+            .map(|i| (i, data.vectors[i].iter().zip(q).map(|(a, b)| a * b).sum()))
+            .collect();
+        s.sort_by(|a, b| b.1.total_cmp(&a.1).then(a.0.cmp(&b.0)));
+        s.into_iter().take(K).map(|(i, _)| i).collect()
+    };
+
+    // Two stores: channel = random bucket, and channel = cluster.
+    let dir = tempfile::tempdir().unwrap();
+    let mut stores = Vec::new();
+    for by_cluster in [false, true] {
+        let mut rng = Rng(3);
+        let entries: Vec<MemoryEntry> = data
+            .vectors
+            .iter()
+            .enumerate()
+            .map(|(i, v)| MemoryEntry {
+                chunk: text_for(data.cluster_of[i], i, &mut rng),
+                embedding: v.clone(),
+                source_channel: if by_cluster {
+                    format!("c{}", data.cluster_of[i])
+                } else {
+                    format!("b{}", bucket[i])
+                },
+                timestamp: i as f64,
+                session_id: format!("s{}", i % 50),
+                tags: format!("t{i}"),
+            })
+            .collect();
+        let mut config = MemoryConfig::new(
+            dir.path().join(format!("opt_{by_cluster}.h5")),
+            "bench",
+            DIM,
+        );
+        config.hebbian_boost = 0.0;
+        let mut mem = HDF5Memory::create(config).unwrap();
+        mem.save_batch(entries).unwrap();
+        std::hint::black_box(mem.search(&data.queries[0], "", &SearchOptions::new(K)));
+        stores.push(mem);
+    }
+
+    let vector_only = SearchOptions::new(K).with_fusion(Fusion::Weighted {
+        vector: 1.0,
+        keyword: 0.0,
+    });
+    // (label, store, channels for query i, allowed(i, record))
+    type Case<'a> = (
+        String,
+        usize,
+        Box<dyn Fn(usize) -> Option<Vec<String>> + 'a>,
+        Box<dyn Fn(usize, usize) -> bool + 'a>,
+    );
+    let mut cases: Vec<Case> = vec![(
+        "no filter".into(),
+        0,
+        Box::new(|_| None),
+        Box::new(|_, _| true),
+    )];
+    for pct in [50usize, 10, 1] {
+        cases.push((
+            format!("random {pct}%"),
+            0,
+            Box::new(move |_| Some((0..pct).map(|b| format!("b{b}")).collect())),
+            Box::new(move |_, i| bucket[i] < pct),
+        ));
+    }
+    let d = &data;
+    let away = move |qi: usize| {
+        let qc = d.query_cluster[qi];
+        [
+            (qc + n_clusters / 3) % n_clusters,
+            (qc + 2 * n_clusters / 3) % n_clusters,
+        ]
+    };
+    cases.push((
+        "2 clusters away from the query".into(),
+        1,
+        Box::new(move |qi| Some(away(qi).iter().map(|c| format!("c{c}")).collect())),
+        Box::new(move |qi, i| away(qi).contains(&d.cluster_of[i])),
+    ));
+
+    for (label, store, channels, allowed) in &cases {
+        let mem = &mut stores[*store];
+        let mut hits = 0;
+        let mut kept = 0;
+        for (qi, q) in data.queries.iter().enumerate() {
+            let mut opts = vector_only.clone();
+            opts.source_channels = channels(qi);
+            let got = mem.search(q, "", &opts);
+            let want = exact_top(q, &|i| allowed(qi, i));
+            kept += want.len();
+            hits += got.iter().filter(|r| want.contains(&r.index)).count();
+        }
+        let latency = summarize(
+            (0..N_QUERIES)
+                .map(|qi| {
+                    let mut opts = SearchOptions::new(K);
+                    opts.source_channels = channels(qi);
+                    let t = Instant::now();
+                    std::hint::black_box(mem.search(&data.queries[qi], &query_texts[qi], &opts));
+                    t.elapsed()
+                })
+                .collect(),
+        );
+        println!(
+            "| {n} | {label} | {:.4} | {:.3} | {:.3} |",
+            hits as f64 / kept.max(1) as f64,
+            millis(latency.p50),
+            millis(latency.p99),
+        );
+    }
+
+    let mem = &mut stores[0];
+    for (label, opts) in [
+        (
+            "re-rank",
+            SearchOptions::new(K).with_rerank(ReRankConfig::default()),
+        ),
+        (
+            "re-rank + confidence",
+            SearchOptions::new(K)
+                .with_rerank(ReRankConfig::default())
+                .with_confidence(ConfidenceConfig::default()),
+        ),
+    ] {
+        let latency = summarize(
+            (0..N_QUERIES)
+                .map(|qi| {
+                    let t = Instant::now();
+                    std::hint::black_box(mem.search(&data.queries[qi], &query_texts[qi], &opts));
+                    t.elapsed()
+                })
+                .collect(),
+        );
+        println!(
+            "| {n} | {label} | — | {:.3} | {:.3} |",
+            millis(latency.p50),
+            millis(latency.p99)
+        );
+    }
+}
+
 // ---------------------------------------------------------------------------
 // float16 study: what does half-precision embedding storage cost?
 // ---------------------------------------------------------------------------
@@ -788,6 +960,19 @@ fn main() {
         }
         return;
     }
+    if args.iter().any(|a| a == "--options-study") {
+        println!("## Search options ({DIM}-dim, k = {K}, Hebbian boost off)\n");
+        println!("| N | options | filtered recall@10 | p50 ms | p99 ms |");
+        println!("|---:|---|---:|---:|---:|");
+        for &n in if full {
+            &[10_000, 100_000][..]
+        } else {
+            &[10_000][..]
+        } {
+            options_study(n);
+        }
+        return;
+    }
     if args.iter().any(|a| a == "--f16-first") {
         F16_FIRST.store(true, std::sync::atomic::Ordering::Relaxed);
     }

crates/clawhdf5-cli/src/main.rs

@@ -36,10 +36,13 @@ enum Commands {
         /// Accepted for compatibility; int8 is now the default
         #[arg(long, hide = true, conflicts_with = "f32_index")]
         quantized_index: bool,
-        /// Store embeddings on disk as IEEE half precision (float16): half
-        /// the bytes, about three significant digits; values must lie within
-        /// ±65504
+        /// Store embeddings as full-precision f32 instead of the default
+        /// half precision (float16: half the bytes, about three significant
+        /// digits, values within ±65504)
         #[arg(long)]
+        f32: bool,
+        /// Accepted for compatibility; float16 is now the default
+        #[arg(long, hide = true, conflicts_with = "f32")]
         float16: bool,
     },
     /// Save a memory entry (reads JSON from stdin or --json)
@@ -107,11 +110,16 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
             wal,
             f32_index,
             quantized_index: _,
-            float16,
+            f32,
+            float16: _,
         } => {
             let mut config = MemoryConfig::new(cli.path.clone(), &agent_id, dim);
             config.wal_enabled = wal;
-            config.float16 = float16;
+            // As with --f32-index: only ever switch the library default off.
+            if f32 {
+                config.float16 = false;
+            }
+            let config_float16 = config.float16;
             // Only ever switch *off* the library default: assigning the flag
             // outright would force every CLI-created store back to f32 unless
             // the caller knew to ask for int8.
@@ -127,7 +135,7 @@ fn run(cli: Cli) -> Result<(), Box<dyn std::error::Error>> {
                 "embedding_dim": dim,
                 "wal_enabled": wal,
                 "quantized_index": config_quantized,
-                "float16": float16,
+                "float16": config_float16,
                 "count": mem.count(),
             });
             println!("{}", serde_json::to_string_pretty(&j)?);