diff --git a/CHECKLIST.md b/CHECKLIST.md
index 17a7c33..181706d 100644
--- a/CHECKLIST.md
+++ b/CHECKLIST.md
@@ -75,9 +75,9 @@
   - [x] p0-3.4 七个过滤器（holder/scenario/source/modality/since/until/min_trust）+ base64 游标；`since > until` 报 "search since must not be after until"，游标坏了报 "search cursor is unreadable"
   - [x] p0-3.5 RRF（`RRF_K = 60`）取代 `0.55/0.35/0.10`；顺手删掉无人调用的 `tokenize()` / `lexical_score()` / `_graph_score()`（graph 项恒为 0，因为证据边从不参与排序）
   - [x] p0-3.6 Go 的 `search_atoms` / `compile_context` 改走 `POST /api/search`，删掉 Go 侧第二套分词器（`store.SearchAtoms` / `ftsQuery` / `tokenPattern`）——两面不可能再漂开，因为只剩一个排序实现
-  - [x] p0-3.7 100k atom 下 p50 <100ms：`python -m bench.scale_check` → `bench/reports/SCALE.md`。实测 **165,973 atom（bar 的 1.66 倍）、300 探针、p50 57ms / p95 138ms**，机器 load 1.76 / 32 核 / 每查询 4 线程；基线 p50 20.16s ⇒ **快 343 倍**（SCALE.md 重跑：p50 58.7ms / p95 143.0ms）。同一探针集上的质量与延迟扫描见 `bench/reports/TERMS.md`（n=173 holder 作用域，0.05 档 recall@10 0.803、MRR 0.652、p50 64.8ms / p95 121.7ms）。
+  - [x] p0-3.7 100k atom 下 p50 <100ms：`python -m bench.scale_check` → `bench/reports/SCALE.md`。报告那一次实测 **165,973 atom（bar 的 1.66 倍）、300 探针、p50 71.7ms / p95 217.7ms / max 780.5ms**，机器 load [2.31, 3.72, 7.9] / 32 核 / 每查询 4 线程；基线 p50 20.16s ⇒ **快 281 倍**。同一脚本在更空的机器上跑到过 p50 56.5ms / 58.7ms（load 1.5–1.8）——延迟随负载走，所以引用报告那一次，不挑最好看的。同一探针集上的质量与延迟扫描见 `bench/reports/TERMS.md`（n=173 holder 作用域，0.05 档 recall@10 0.803、MRR 0.652、p50 69.5ms / p95 131.5ms）。
     - 根因不是 SQL 慢，是 OR 表达式里那些「每个文档都有」的虚词：`"parsing"* OR "error"* OR "in"* OR "the"* …` 命中 127,145 行、305ms，而中文二元组短语查询 32 命中只要 1.0ms。bm25 必须给每条命中打分，延迟就跟着命中数走。
-    - 修法：用索引自己当 IDF 源，按文档频率丢掉虚词（`text_index.rare_terms`，`COMMON_TERM_FRACTION = 0.05`，无手写停用词表，换语料自动换答案）。0.05 是扫出来的，扫描器已入库成证据：`bench/term_sweep.py` → `bench/reports/TERMS.md`（n=173，冻结库的副本）：0.20→0.792/385.9ms、0.10→0.792/372.9ms、**0.05→0.803/64.8ms**、0.02→0.792/30.3ms、0.01→0.775/16.8ms、0.005→0.740/9.3ms —— 召回峰值就在 0.05。早先 scratch 脚本扫出的 0.893/0.899/0.870 已被取代：那些数字没有入库的产物，且是在检索改写（候选放宽规则、向量腿入库）之前测的。为此 `rare_terms(connection, terms, *, fraction=None)` 改成调用时解析上界——def 期默认值扫不动。
+    - 修法：用索引自己当 IDF 源，按文档频率丢掉虚词（`text_index.rare_terms`，`COMMON_TERM_FRACTION = 0.05`，无手写停用词表，换语料自动换答案）。0.05 是扫出来的，扫描器已入库成证据：`bench/term_sweep.py` → `bench/reports/TERMS.md`（n=173，冻结库的副本）：0.20→0.792/420.3ms、0.10→0.792/467.5ms、**0.05→0.803/69.5ms**、0.02→0.792/33.4ms、0.01→0.775/19.2ms、0.005→0.740/10.8ms —— 召回峰值就在 0.05。早先 scratch 脚本扫出的 0.893/0.899/0.870 已被取代：那些数字没有入库的产物，且是在检索改写（候选放宽规则、向量腿入库）之前测的。为此 `rare_terms(connection, terms, *, fraction=None)` 改成调用时解析上界——def 期默认值扫不动。
     - 计数缓存 `term_df`（schema v6）：一个词只数一次，任何 atom 写入都清空它，所以缓存不会比它所描述的库更老。冷缓存 p50 73ms / 热 63ms，1,324 个词被缓存。
   - 验收：`pytest backend/tests/test_text_index.py backend/tests/test_search_api.py` 绿（18 条，真 FTS5 表 + TestClient）：「钥匙」命中「我们的钥匙放在玄关抽屉里」、`search atoms` 命中 `func SearchAtoms(ctx context.Context)`、`forg` 命中 forgotten、全角 `Ｓｅａｒｃｈ　Ａｔｏｍｓ` 命中、游标翻页无重复、supersede 后旧记忆不再应答；MCP 一致性由 `ely-eye-mcp/internal/mcpserver/search_tools_test.go`（3 条）+ `bash scripts/mcp_write_smoke.sh` PASS 证明；偏差：checklist 写的 `tests/test_retrieval_parity.py` 不存在，实际在 `backend/tests/` 与 Go 测试里
 - [x] **p0-4** 卡带变成可挂载作用域
@@ -349,12 +349,18 @@
     - `python -m bench.quant_probe` → `bench/reports/QUANT.md`（165,973 向量、200 探针、top 10，tie-aware 一致度）：sqlite-vec float 40.3ms/1.0、+holder 过滤 29.3ms/0.599、int8 35.3ms/**0.977**、int8+holder 23.0/0.595、bit 2.7ms/0.728、bit+holder 4.2/0.513、numpy float32 全扫（真值）8.0ms/1.0、numpy 符号位 hamming 19.1/0.011、float32 前 192/128/64 维 4.4/0.722、4.0/0.57、4.0/0.43、**bit 粗排@200 → numpy 里 float32 精排 23.7ms/0.983**。每向量字节 f32 1536 / int8 384 / bit 48。
     - 结论：int8 省 4× 空间几乎不掉质量（0.977）但不省时间；bit 省 32× 空间和 14× 时间、只剩 0.728，只能当粗排；两级方案必须在 vec0 **外面**精排（在库里用 `rowid IN (…)` 回捞反而更慢）。MRL 截断是在**不是 MRL 训练**的 MiniLM 上测的，所以那是真实代价不是白拿；切片必须是存下来的连续维，不是查询时切（非连续视图点集会掉速度）。
     - 偏差：精排腿（`bge-reranker-base`）早在基线消融里就有并测过（加了它 recall@10 从 0.565 掉到 0.453），所以这里没再为它编一套故事；粗排+精排这条链测出 0.983 一致度但**没接线**，因为 dense 腿默认关（见下）。
-  - [x] p2-1.3 100k 实测 + 1M 外推：`python -m bench.dense_scale` → `bench/reports/DENSE.md`（stamp cd9851a）
-    - 实测 **165,973 atom / 153,857 向量**：建库 1599.1s（96 向量/秒）、**检索 p50 74.6ms / p95 77.5ms / max 89.0ms（300 查询）**、其中编码问题 p50 11.3ms、与暴力扫描 tie-aware 一致度 **0.998**（40 探针）、**往满索引里写一条记忆 p50 514ms / p95 536ms（50 次写）**、失配扫描 1.7s（不在检索路径上）、**每向量 1,729 字节**（dbstat 数 vec0 影子表：266,059,776 B）。
-    - 1M 外推：p50 443ms / p95 463ms、向量表 1.6 GiB、首次建库 154.2 分钟、单条写 3132ms，假设随行印出（逐条扫 ⇒ 线性）。
+  - [x] p2-1.3 100k 实测 + 1M 外推：`python -m bench.dense_scale` → `bench/reports/DENSE.md`（stamp dff5977）
+    - 实测 **165,973 atom / 153,857 向量**：建库 1421.0s（108 向量/秒）、**检索 p50 75.6ms / p95 93.0ms / max 123.6ms（300 查询）**、其中编码问题 p50 12.4ms / p95 25.8ms、与暴力扫描 tie-aware 一致度 **0.998**（40 探针）、**往满索引里写一条记忆 p50 518ms / p95 543ms（50 次写）**、失配扫描 1.3s（不在检索路径上）、**每向量 1,729 字节**（dbstat 数 vec0 影子表：266,059,776 B）、机器 load [3.18, 4.58, 5.42]。
+    - 1M 外推：p50 455ms / p95 561ms、向量表 1.6 GiB、首次建库 153.9 分钟、单条写 3123ms（vec0 按 id 删除也要扫表，所以写同样随规模线性），假设随行印出（逐条扫 ⇒ 线性）。
     - tie-aware 的必要性：语料里有重复 atom，分数并列 ⇒ top-K 的 id 集合本身有歧义，两个都对引擎按 id 比会读出 0.827 的假召回。判定改成「返回的 atom 真实 cosine ≥ 第 K 名 cosine」。
-  - 验收：100k dense 腿 p50 <100ms → **165,973 atom（bar 的 1.66 倍）p50 74.6ms**（DENSE.md）；新 atom 立即可查 → `pytest backend/tests/test_vector_index.py`（写进去当场能搜到、视图藏起来的 dense 腿也藏、备份带走向量恢复后照样应答）。
-  - 数字逼出来的决定：**dense 腿默认关**（`config.py:32 dense_embeddings_enabled=False`）。同一 300 条 holder 作用域探针上，开腿 recall@10 0.832→0.845、MRR 0.683→0.663、p50 64ms→482ms。多 1.3 个点的召回、慢 7 倍、首位质量还降——默认值就该是关，腿留着（已增量、已入库）等更大的语料或更好的嵌入模型。
+  - 验收：100k dense 腿 p50 <100ms → **165,973 atom（bar 的 1.66 倍）p50 75.6ms**（DENSE.md）；新 atom 立即可查 → `pytest backend/tests/test_vector_index.py`（写进去当场能搜到、视图藏起来的 dense 腿也藏、备份带走向量恢复后照样应答）。
+  - [x] p2-1.4 每条腿值多少钱：`python -m bench.leg_check` → `bench/reports/LEGS.md`（stamp dff5977，300 条 holder 作用域探针，7 个配置，每臂带 n / MRR / Wilson 下界 / p50 / p95 / 分模态召回）
+    - 只有词的 FTS 页：问 10 个 atom recall@10 **0.832**、MRR 0.683、p50 65.5ms；问产品配置的 24 个 **0.848** / 0.688 / 72.5ms；页放宽到 30 **0.855** / 0.690 / 64.7ms（放宽不要钱，10→24 就白捡 1.6 个点）。
+    - 开 dense 腿：10 个 atom **0.845** / MRR 0.663 / p50 472.9ms；页 30 **0.858** / 0.669 / 413.7ms。多 1.3–2.6 个点召回、MRR 降、慢 6–7 倍。
+    - 开图片腿（CLIP 索引 53,668 张图、建库 2901.7s、512 维）：10 个 atom **0.815** / MRR 0.553 / 132.5ms；页 30 **0.825** / 0.551 / 162.8ms。分模态才看得见它在干什么：**video 0.583→0.708、pdf 0.849→0.907 涨，code 0.953→0.898、ui_screenshot 0.583→0.500 跌**——它救的正是金标是图的题，代价是稀释别的。
+    - 产品的图片腿不是开关：`retrieval.py:136` `if image is None and not thin: return {}`，稀疏页薄到 2 条以下才问图。所以这几行测的是「腿可用」，不是强行常开融合。
+    - **没达到的验收线，写在这里**：用户定的验收是 recall@10 ≥0.90（n≥300）。测出来最好的一行是 **0.858**（dense 腿开、页 30、p50 414ms），默认配置 **0.848**（p50 72.5ms），CURRENT.md 里按 10 个 atom 问是 0.832；仓内 Mem0/Zep 式对照在 holder 作用域是 **0.866**。差距集中在图（image 0.750、ui_screenshot 0.500–0.583、video 0.583–0.708），文字的题（code 0.953、pdf 0.919）已经过了线。诚实结论：**延迟达标（281 倍于基线、p50 71.7ms），召回没达标**，剩下 4 个点是跨模态召回，不是索引规模问题。
+  - 数字逼出来的决定：**dense 腿默认关**（`config.py:32 dense_embeddings_enabled=False`）。同一 300 条 holder 作用域探针上（LEGS.md），开腿 recall@10 0.832→0.845、MRR 0.683→0.663、p50 65.5ms→472.9ms。多 1.3 个点的召回、慢 7 倍、首位质量还降——默认值就该是关，腿留着（已增量、已入库）等更大的语料或更好的嵌入模型。
   - 顺带修的测量缺陷：`copy_store()` 只拷 SQLite，所以任何要跑视觉腿的 harness 都得自己带上 `objects/`（25 GB，探针里用符号链接）与 `cache/clip_image_index.npy`；`bench/scale_check.py` 原本直接打开冻结基线库、把它**原地迁移**了，现改成在副本上测（`--scratch`）。
 - [ ] **p2-2** 安全与隐私
   - [ ] p2-2.1 FastAPI 鉴权 + Origin 校验
diff --git a/bench/reports/CURRENT.md b/bench/reports/CURRENT.md
index 5307ffd..f58ba6b 100644
--- a/bench/reports/CURRENT.md
+++ b/bench/reports/CURRENT.md
@@ -1,21 +1,19 @@
 # CURRENT: what the product retrieves at this build
 
-- generated 2026-10-07T21:24:15+00:00 at commit dfc6cd4c2889
-- measured store schema: v5
+- generated 2026-10-08T20:17:12+00:00 at commit dff5977ab638
+- measured store schema: v8
 - corpus 50278 sources, 5308 queries (2928 answerable)
 - models: dense `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`, rerank `BAAI/bge-reranker-base`
-- machine: 32 cores, 4 threads per query, load average 1.2 when the report was written
+- machine: 32 cores, 4 threads per query, load average 5.1 when the report was written
 
 | arm | recall@1 | recall@5 | recall@10 | nDCG@10 | MRR | hit@10 (95% lower) | p50 s | p95 s |
 |---|---|---|---|---|---|---|---|---|
 | chance (random ranking) | 0.000 | 0.000 | 0.000 | - | - | - | - | - |
-
-_re-timed on a stride sample with warm models; recall comes from the full pass (n=600 queries)_
-| bm25_only | 0.313 | 0.465 | 0.519 | 0.415 | 0.383 | 0.524 (0.505) | 0.631 | 2.768 |
-| dense_only | 0.227 | 0.363 | 0.424 | 0.321 | 0.290 | 0.429 (0.411) | 0.081 | 0.134 |
-| graph_only | 0.000 | 0.001 | 0.001 | 0.000 | 0.000 | 0.001 (0.000) | 0.055 | 0.095 |
-| no_rerank | 0.342 | 0.510 | 0.565 | 0.452 | 0.418 | 0.571 (0.553) | 0.643 | 2.786 |
-| fusion_rerank | 0.236 | 0.391 | 0.453 | 0.341 | 0.309 | 0.459 (0.441) | 7.000 | 13.247 |
+| bm25_only | 0.313 | 0.465 | 0.519 | 0.415 | 0.383 | 0.524 (0.505) | 0.563 | 3.141 |
+| dense_only | 0.227 | 0.363 | 0.424 | 0.321 | 0.290 | 0.429 (0.411) | 1.728 | 2.883 |
+| graph_only | 0.000 | 0.001 | 0.001 | 0.000 | 0.000 | 0.001 (0.000) | 0.043 | 0.058 |
+| no_rerank | 0.342 | 0.510 | 0.565 | 0.452 | 0.418 | 0.571 (0.553) | 2.404 | 5.169 |
+| fusion_rerank | 0.236 | 0.391 | 0.453 | 0.341 | 0.309 | 0.459 (0.441) | 4.538 | 9.043 |
 
 ## recall@10 by modality
 
@@ -35,24 +33,12 @@ _re-timed on a stride sample with warm models; recall comes from the full pass (
 - no_rerank: abstains on 0% of unanswerable queries, and its top hit comes from the query's own holder for 40% of them; the rest are confident answers pulled out of somebody else's memory
 - fusion_rerank: abstains on 0% of unanswerable queries, and its top hit comes from the query's own holder for 34% of them; the rest are confident answers pulled out of somebody else's memory
 
-## the unmodified product path
+## the product path, asked as its holder
 
-- RetrievalService.search: p50 0.68s, mean 1.08s, max 9.36s over 40 queries
-- top-10 overlap with the fast BM25 path: 0.0%, identical order 0.0%
-- its top-10 atoms cover only 7.7 distinct sources (min 1): the product ranks atoms and never diversifies, so ten slots fill with fragments of one document and the overlap with a source-level ranking is near zero
-
-## latency vs corpus size
-
-| sources | atoms | bm25_only p50/p95 s | dense_only p50/p95 s |
-|---|---|---|---|
-| 500 | 500 | 0.001/0.003 | 0.018/0.031 |
-| 2000 | 2000 | 0.005/0.010 | 0.045/0.078 |
-| 10000 | 10000 | 0.035/0.110 | 0.079/0.100 |
-| 50000 | 165695 | 0.944/2.376 | 0.090/0.108 |
-
-_latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless_
-
-_latency was measured at load average 1.2 on 32 cores; a busy machine inflates these figures, so read them as a ceiling_
+- RetrievalService.search as the query's own holder: recall@10 0.832, MRR 0.683, hit@10 0.833 (95% lower 0.787) over n=300 answerable queries
+- latency p50 0.074s, p95 0.146s, mean 0.079s, max 0.314s
+- top-10 overlap with the fast BM25 path: 13.4%, identical order 0.0%
+- its top-10 atoms cover 5.2 distinct sources on average (min 0): the product ranks atoms and never diversifies, so ten slots can fill with fragments of one document
 
 ## reading
 
diff --git a/bench/reports/DENSE.md b/bench/reports/DENSE.md
new file mode 100644
index 0000000..bb8bec9
--- /dev/null
+++ b/bench/reports/DENSE.md
@@ -0,0 +1,23 @@
+# DENSE: the vector leg at corpus size (commit dff5977ab)
+
+- database: `bench/runs/dense_scale/data/ely_eye.sqlite`
+- atoms: 165973, memories with words: 153857, vectors: 153857, pending: 0
+- model: `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`, width 384
+- build: 1421.0s for 153857 memories (108 per second)
+- writing one memory into a full index: p50 518ms · p95 543ms over 50 writes
+- the repair scan (`ely-eye build-dense-index`) over a full index: 1.3s, and it is not on the search path
+- search p50 75.6ms · p95 93.0ms · max 123.6ms over 300 queries
+- of which encoding the question: p50 12.4ms · p95 25.8ms
+- against a brute-force scan: 0.998 of the top 10 over 40 probes (a returned atom counts when its true cosine is at least the k-th best, so ties cannot fail the check)
+- bytes per stored vector: 1729 (266059776 bytes for 153857 vectors, dbstat page sizes over the vec0 shadow tables)
+- machine: Linux 6.18.40.1-microsoft-standard-WSL2, 32 cores, 4 threads per query, load [3.18, 4.58, 5.42]
+
+## One million memories on this machine
+
+- p50 455ms · p95 561ms
+- vector table: 1.6 GiB
+- first build: 153.9 minutes
+- writing one memory: 3123ms — a vec0 delete by id scans the table, so this grows with the count too
+- assumption: sqlite-vec 0.1.9 scans every vector, so latency and bytes grow linearly with the count; the model, the machine and the thread budget stay the same
+
+_Latency is wall time per search on the machine named above with the thread budget the benchmark pins; a run on a loaded machine is not comparable._
diff --git a/bench/reports/LEGS.md b/bench/reports/LEGS.md
new file mode 100644
index 0000000..1a1ed95
--- /dev/null
+++ b/bench/reports/LEGS.md
@@ -0,0 +1,37 @@
+# LEGS: what each retrieval leg is worth
+
+- commit: `dff5977ab638896cc8685c78ee41e5195fc0fbf2`, schema v8
+- store: bench/runs/leg_check/data/ely_eye.sqlite (a copy of bench/runs/dense_scale; the frozen baseline is never opened)
+- 300 answerable queries, each asked as the holder the frozen set assigns it
+- machine: Linux 6.18.40.1-microsoft-standard-WSL2, 32 cores, 4 threads per query, load [25.51, 9.62, 5.7]
+
+Recall is scored on the first ten **sources** after atoms are collapsed to their source
+by first hit, which is how every other arm in this repo is scored.
+
+| leg configuration | atoms asked | recall@10 | MRR | hit@10 | 95% lower | p50 ms | p95 ms | distinct sources |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| words only | 10 | 0.832 | 0.683 | 0.833 | 0.787 | 65.5 | 134.9 | 4.0 |
+| words only (the configured 24) | 24 | 0.848 | 0.688 | 0.850 | 0.805 | 72.5 | 155.9 | 6.0 |
+| words only, wider page | 30 | 0.855 | 0.690 | 0.857 | 0.812 | 64.7 | 129.7 | 6.0 |
+| + dense leg | 10 | 0.845 | 0.663 | 0.847 | 0.802 | 472.9 | 577.5 | 5.0 |
+| + dense leg, wider page | 30 | 0.858 | 0.669 | 0.860 | 0.816 | 413.7 | 525.8 | 7.0 |
+| + picture leg | 10 | 0.815 | 0.553 | 0.817 | 0.769 | 132.5 | 271.3 | 8.0 |
+| + picture leg, wider page | 30 | 0.825 | 0.551 | 0.827 | 0.780 | 162.8 | 283.7 | 20.0 |
+
+## recall@10 by modality
+
+| leg configuration | code | image | pdf | ui_screenshot | video |
+| --- | --- | --- | --- | --- | --- |
+| words only | 0.953 | 0.750 | 0.849 | 0.583 | 0.583 |
+| words only (the configured 24) | 0.953 | 0.750 | 0.895 | 0.583 | 0.625 |
+| words only, wider page | 0.953 | 0.750 | 0.919 | 0.583 | 0.625 |
+| + dense leg | 0.938 | 0.750 | 0.884 | 0.639 | 0.625 |
+| + dense leg, wider page | 0.938 | 0.750 | 0.919 | 0.639 | 0.667 |
+| + picture leg | 0.930 | 0.750 | 0.849 | 0.528 | 0.583 |
+| + picture leg, wider page | 0.898 | 0.750 | 0.907 | 0.500 | 0.708 |
+
+_Query counts per modality: code 128, image 26, pdf 86, ui_screenshot 36, video 24._
+
+The picture leg is not a switch this harness flips per row: the product runs it only when the sparse page comes back thin, so these rows measure the product with the leg available, not a forced always-on fusion.
+
+_Latency is wall time per search on the machine named above with the thread budget the benchmark pins; a run on a loaded machine is not comparable._
diff --git a/bench/reports/QUANT.md b/bench/reports/QUANT.md
new file mode 100644
index 0000000..023493c
--- /dev/null
+++ b/bench/reports/QUANT.md
@@ -0,0 +1,28 @@
+# QUANT: what each vector encoding buys (commit cd9851a12)
+
+- corpus: 165973 vectors, 384 dimensions, 200 probes, top 10
+- machine: Linux 6.18.40.1-microsoft-standard-WSL2, 32 cores, 4 threads per query, load [2.46, 4.17, 4.58]
+- probes are drawn from the corpus itself, so these arms are compared with each other, not quoted as retrieval quality; a hit is any returned atom whose true cosine is at least the 10-th best, because byte-identical atoms make the id set ambiguous
+
+| arm | p50 ms | p95 ms | recall@10 |
+| --- | --- | --- | --- |
+| sqlite-vec float | 41.2 | 55.4 | 1.0 |
+| sqlite-vec float + holder filter | 29.8 | 30.7 | 0.599 |
+| sqlite-vec int8 | 35.5 | 42.2 | 0.977 |
+| sqlite-vec int8 + holder filter | 22.3 | 28.1 | 0.595 |
+| sqlite-vec bit | 2.6 | 2.8 | 0.728 |
+| sqlite-vec bit + holder filter | 4.1 | 4.3 | 0.513 |
+| numpy float32 scan (the truth) | 8.0 | 9.6 | 1.0 |
+| numpy hamming (sign bit) | 18.9 | 19.8 | 0.011 |
+| numpy float32 first 192 dims | 7.5 | 8.2 | 0.722 |
+| numpy float32 first 128 dims | 7.9 | 8.9 | 0.57 |
+| numpy float32 first 64 dims | 4.0 | 8.3 | 0.43 |
+| bit coarse@200 -> float32 rerank in numpy | 23.7 | 24.7 | 0.983 |
+
+| encoding | bytes per vector |
+| --- | --- |
+| float32 | 1536 |
+| int8 | 384 |
+| bit | 48 |
+
+_Latency is wall time per query on the machine named above with the thread budget the benchmark pins; a run on a loaded machine is not comparable._
diff --git a/bench/reports/SCALE.md b/bench/reports/SCALE.md
index 980201a..6746d70 100644
--- a/bench/reports/SCALE.md
+++ b/bench/reports/SCALE.md
@@ -1,10 +1,14 @@
 # SCALE: the product's own search at corpus size
 
-- database: bench/runs/baseline/data/ely_eye.sqlite
+- commit: `dff5977ab638896cc8685c78ee41e5195fc0fbf2`
+- database: bench/runs/scale/data/ely_eye.sqlite
+- copied from the frozen baseline in 5.0s and migrated in 0.0s to schema 8; the baseline store itself is left untouched
 - atoms: 165973, probes: 300 (code, image, pdf, ui_screenshot, video)
 - dense embeddings: False
-- opening the store (migrations included): 0.000s
-- p50 0.0570s · p95 0.1383s · max 0.5395s over 300 queries
-- machine: Linux 6.18.40.1-microsoft-standard-WSL2, 32 cores, 4 threads per query, load [1.77, 1.62, 1.64]
+- p50 0.0717s · p95 0.2177s · max 0.7805s over 300 queries
+- machine: Linux 6.18.40.1-microsoft-standard-WSL2, 32 cores, 4 threads per query, load [2.31, 3.72, 7.9]
+
+The baseline recorded p50 20.16s on this corpus because the old search pulled every
+atom into Python and rebuilt a BM25 index per query.
 
 _Latency is wall time per search call on an idle machine with the thread budget the benchmark pins; a run on a loaded machine is not comparable._
diff --git a/bench/reports/TERMS.md b/bench/reports/TERMS.md
new file mode 100644
index 0000000..2b4dd43
--- /dev/null
+++ b/bench/reports/TERMS.md
@@ -0,0 +1,20 @@
+# TERMS: which query words are worth paying for
+
+- commit: `dff5977ab638896cc8685c78ee41e5195fc0fbf2`
+- store: bench/runs/term_sweep/data/ely_eye.sqlite (copied from the frozen baseline in 5.1s)
+- 173 answerable probes, each asked as the holder the frozen set assigns it, ranked by the unmodified `RetrievalService`
+- the bound the product ships is 0.05; the measured recall peak sits at 0.05
+- machine: Linux 6.18.40.1-microsoft-standard-WSL2, 32 cores, 4 threads per query, load [2.32, 3.87, 8.09]
+
+| bound | recall@10 | MRR | hit@10 | p50 ms | p95 ms | median distinct sources |
+| --- | --- | --- | --- | --- | --- | --- |
+| 0.200 | 0.792 | 0.629 | 0.792 | 420.3 | 1704.1 | 6 |
+| 0.100 | 0.792 | 0.632 | 0.792 | 467.5 | 1747.5 | 6 |
+| 0.050 **(shipped)** | 0.803 | 0.652 | 0.803 | 69.5 | 131.5 | 5 |
+| 0.020 | 0.792 | 0.631 | 0.792 | 33.4 | 88.4 | 4 |
+| 0.010 | 0.775 | 0.617 | 0.775 | 19.2 | 63.0 | 4 |
+| 0.005 | 0.740 | 0.563 | 0.740 | 10.8 | 69.9 | 3 |
+
+The bound is a share of the library: a word carried by more atoms than that is dropped from the OR query, because bm25 scores every row the query touches. It is relative to the corpus on purpose — a corpus of PDFs and a corpus of shell history disagree about which of their own words are noise.
+
+_Latency is wall time per search on the machine named above with the thread budget the benchmark pins; a run on a loaded machine is not comparable._
diff --git a/bench/reports/current.json b/bench/reports/current.json
index d6f5f6d..5b3ac62 100644
--- a/bench/reports/current.json
+++ b/bench/reports/current.json
@@ -89,12 +89,11 @@
     }
    },
    "latency_seconds": {
-    "max": 10.484337914007483,
-    "mean": 0.9426860555750318,
-    "note": "re-timed on a stride sample with warm models; recall comes from the full pass",
-    "p50": 0.63090720949549,
-    "p95": 2.7677196743439674,
-    "queries": 600
+    "max": 70.10108924399901,
+    "mean": 1.033756499582343,
+    "p50": 0.5625866459999997,
+    "p95": 3.1407018873005645,
+    "queries": 5308
    },
    "overall": {
     "abstention_rate_answerable": 0.0,
@@ -200,12 +199,11 @@
     }
    },
    "latency_seconds": {
-    "max": 1.5199895569967339,
-    "mean": 0.09428343026663545,
-    "note": "re-timed on a stride sample with warm models; recall comes from the full pass",
-    "p50": 0.08063396950092283,
-    "p95": 0.13402395970297218,
-    "queries": 600
+    "max": 5.211815150996699,
+    "mean": 1.90850240464544,
+    "p50": 1.7281245969993506,
+    "p95": 2.8826986088994087,
+    "queries": 5308
    },
    "overall": {
     "abstention_rate_answerable": 0.0,
@@ -311,12 +309,11 @@
     }
    },
    "latency_seconds": {
-    "max": 0.11273866199917393,
-    "mean": 0.06529701035842057,
-    "note": "re-timed on a stride sample with warm models; recall comes from the full pass",
-    "p50": 0.05544837899287813,
-    "p95": 0.09468995315801294,
-    "queries": 600
+    "max": 0.09835406600177521,
+    "mean": 0.044743228177486605,
+    "p50": 0.04287036849927972,
+    "p95": 0.05790443480054818,
+    "queries": 5308
    },
    "overall": {
     "abstention_rate_answerable": 0.0,
@@ -422,12 +419,11 @@
     }
    },
    "latency_seconds": {
-    "max": 10.533955075996346,
-    "mean": 0.9619427272766673,
-    "note": "re-timed on a stride sample with warm models; recall comes from the full pass",
-    "p50": 0.6425201155034301,
-    "p95": 2.786401089788338,
-    "queries": 600
+    "max": 72.48293514099714,
+    "mean": 2.882459499187649,
+    "p50": 2.40399285599824,
+    "p95": 5.168583710499833,
+    "queries": 5308
    },
    "overall": {
     "abstention_rate_answerable": 0.0,
@@ -533,12 +529,11 @@
     }
    },
    "latency_seconds": {
-    "max": 28.03755501300475,
-    "mean": 7.456139394386567,
-    "note": "re-timed on a stride sample with warm models; recall comes from the full pass",
-    "p50": 7.000474203505291,
-    "p95": 13.246865720593268,
-    "queries": 600
+    "max": 77.63319287999548,
+    "mean": 5.166508504176891,
+    "p50": 4.5375522020012795,
+    "p95": 9.042528758149817,
+    "queries": 5308
    },
    "overall": {
     "abstention_rate_answerable": 0.0,
@@ -563,32 +558,45 @@
   "recall_at_5": 9.944707426707506e-05
  },
  "live_product": {
+  "abstention_rate_answerable": 0.016666666666666666,
+  "abstention_rate_unanswerable": 0.0,
   "distinct_sources_in_top10": {
    "max": 10,
-   "mean": 7.675,
-   "min": 1
+   "mean": 5.193333333333333,
+   "min": 0
   },
+  "hit_at_10": 0.8333333333333334,
+  "hit_at_10_wilson_lower": 0.7870033503873167,
   "identical_top10": 0.0,
-  "max": 9.363575160998153,
-  "mean": 1.0766488450000906,
-  "p50": 0.6765933140050038,
-  "queries": 40,
-  "top10_overlap_with_bm25_only": 0.0,
+  "max": 0.3143954080005642,
+  "mean": 0.07851241317994816,
+  "mrr": 0.6832447089947089,
+  "n_answerable": 300,
+  "n_unanswerable": 0,
+  "ndcg_at_10": 0.7186678703708246,
+  "p50": 0.07425041199894622,
+  "p95": 0.14573381999798585,
+  "queries": 300,
+  "recall_at_1": 0.5966666666666667,
+  "recall_at_10": 0.8316666666666667,
+  "recall_at_5": 0.7816666666666666,
+  "top10_overlap_with_bm25_only": 0.13433333333333336,
+  "top_hit_from_holder_unanswerable": 0.0,
   "top_k": 10
  },
  "meta": {
   "answerable": 2928,
   "corpus_manifest_sha256": "8fc1ed861c891d778853d41107630869f9eb267a669c5d1038e389de80114347",
   "corpus_sources": 50278,
-  "generated_at": "2026-10-07T21:24:15+00:00",
-  "git_commit": "dfc6cd4c2889e872d3f7d983b18406e6ff7a622d",
+  "generated_at": "2026-10-08T20:17:12+00:00",
+  "git_commit": "dff5977ab638896cc8685c78ee41e5195fc0fbf2",
   "limit_per_modality": null,
   "machine": {
    "cpus": 32,
    "load_average_1_5_15": [
-    1.21,
-    1.55,
-    2.28
+    5.09,
+    4.28,
+    8.06
    ],
    "release": "6.18.40.1-microsoft-standard-WSL2",
    "system": "Linux",
@@ -608,64 +616,6 @@
    "ui_screenshot": "2269dd623e67cd851fb3f6b24a4556a8462a37db98e12ad79577c5830167c7c5",
    "video": "4973231d9f16d214b14923d79a34b85c3d2b423624dea752b1ee91ba09ff778c"
   },
-  "store_schema_version_at_start": 5
- },
- "scale": [
-  {
-   "atoms": 500,
-   "bm25_only": {
-    "p50": 0.001200532500661211,
-    "p95": 0.0033308441004919584
-   },
-   "dense_only": {
-    "p50": 0.018240716999571305,
-    "p95": 0.03141984814501483
-   },
-   "note": "latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless",
-   "queries": 40,
-   "sources": 500
-  },
-  {
-   "atoms": 2000,
-   "bm25_only": {
-    "p50": 0.0047556615027133375,
-    "p95": 0.009782735643602785
-   },
-   "dense_only": {
-    "p50": 0.044618372499826364,
-    "p95": 0.07758056885431869
-   },
-   "note": "latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless",
-   "queries": 40,
-   "sources": 2000
-  },
-  {
-   "atoms": 10000,
-   "bm25_only": {
-    "p50": 0.03480406300150207,
-    "p95": 0.11021578034851681
-   },
-   "dense_only": {
-    "p50": 0.07940824350225739,
-    "p95": 0.09990645345169469
-   },
-   "note": "latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless",
-   "queries": 40,
-   "sources": 10000
-  },
-  {
-   "atoms": 165695,
-   "bm25_only": {
-    "p50": 0.9441751839985955,
-    "p95": 2.3757928041479315
-   },
-   "dense_only": {
-    "p50": 0.08988298100302927,
-    "p95": 0.10847034014877863
-   },
-   "note": "latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless",
-   "queries": 40,
-   "sources": 50000
-  }
- ]
+  "store_schema_version_at_start": 8
+ }
 }
\ No newline at end of file
diff --git a/bench/reports/dense.json b/bench/reports/dense.json
new file mode 100644
index 0000000..02fb131
--- /dev/null
+++ b/bench/reports/dense.json
@@ -0,0 +1,73 @@
+{
+  "commit": "dff5977ab638896cc8685c78ee41e5195fc0fbf2",
+  "database": "bench/runs/dense_scale/data/ely_eye.sqlite",
+  "atoms": 165973,
+  "memories_with_words": 153857,
+  "vectors": 153857,
+  "pending": 0,
+  "dim": 384,
+  "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+  "copy_seconds": 0.665644496002642,
+  "build_seconds": 1421.0276969089973,
+  "texts_per_second": 108.2716405420302,
+  "repair_seconds": 1.2946931579936063,
+  "writes": 50,
+  "write_latency": {
+    "queries": 50,
+    "mean": 0.5204645371803781,
+    "p50": 0.5183807220018934,
+    "p95": 0.5429989652060613,
+    "max": 0.5760761429992272
+  },
+  "probes": 300,
+  "probe_modalities": [
+    "code",
+    "image",
+    "pdf",
+    "ui_screenshot",
+    "video"
+  ],
+  "latency": {
+    "queries": 300,
+    "mean": 0.07861847096656371,
+    "p50": 0.07557305500085931,
+    "p95": 0.09303550984986941,
+    "max": 0.123588470989489
+  },
+  "encode_latency": {
+    "queries": 300,
+    "mean": 0.014255492710411393,
+    "p50": 0.012439899503078777,
+    "p95": 0.025788354348333098,
+    "max": 0.0513256230042316
+  },
+  "correctness": {
+    "probes": 40,
+    "top_k": 10,
+    "tie_aware_agreement": 0.9975,
+    "note": "a returned atom counts when its true cosine is at least the k-th best, so ties cannot fail the check"
+  },
+  "bytes_per_vector": 1729.2666307025354,
+  "vector_bytes": 266059776,
+  "bytes_method": "dbstat page sizes over the vec0 shadow tables",
+  "extrapolation": {
+    "to": 1000000,
+    "assumption": "sqlite-vec 0.1.9 scans every vector, so latency and bytes grow linearly with the count; the model, the machine and the thread budget stay the same",
+    "p50_seconds": 0.45533342773137386,
+    "p95_seconds": 0.5605460517666694,
+    "write_p50_seconds": 3.1232834376789804,
+    "gibibytes": 1.6105050507025191,
+    "build_seconds": 9236.028889871746
+  },
+  "machine": {
+    "system": "Linux",
+    "release": "6.18.40.1-microsoft-standard-WSL2",
+    "cpus": 32,
+    "threads_per_query": 4,
+    "load_average_1_5_15": [
+      3.18,
+      4.58,
+      5.42
+    ]
+  }
+}
\ No newline at end of file
diff --git a/bench/reports/leg_check.json b/bench/reports/leg_check.json
new file mode 100644
index 0000000..6f00ead
--- /dev/null
+++ b/bench/reports/leg_check.json
@@ -0,0 +1,208 @@
+{
+  "commit": "dff5977ab638896cc8685c78ee41e5195fc0fbf2",
+  "schema_version": 8,
+  "store": "bench/runs/leg_check/data/ely_eye.sqlite",
+  "probes": 300,
+  "arms": [
+    {
+      "label": "words only",
+      "top_k": 10,
+      "dense": false,
+      "visual": false,
+      "recall_at_10": 0.8316666666666667,
+      "mrr": 0.6832447089947089,
+      "hit_at_10": 0.8333333333333334,
+      "wilson_lower": 0.7870033503873167,
+      "p50_seconds": 0.0654734540003119,
+      "p95_seconds": 0.13490134734311146,
+      "median_distinct_sources": 4.0,
+      "by_modality": {
+        "code": 0.953125,
+        "image": 0.75,
+        "pdf": 0.8488372093023255,
+        "ui_screenshot": 0.5833333333333334,
+        "video": 0.5833333333333334
+      },
+      "modality_counts": {
+        "code": 128,
+        "image": 26,
+        "pdf": 86,
+        "ui_screenshot": 36,
+        "video": 24
+      }
+    },
+    {
+      "label": "words only (the configured 24)",
+      "top_k": 24,
+      "dense": false,
+      "visual": false,
+      "recall_at_10": 0.8483333333333334,
+      "mrr": 0.6875571326821327,
+      "hit_at_10": 0.85,
+      "wilson_lower": 0.8051823888546709,
+      "p50_seconds": 0.07251374649786158,
+      "p95_seconds": 0.15588207065375176,
+      "median_distinct_sources": 6.0,
+      "by_modality": {
+        "code": 0.953125,
+        "image": 0.75,
+        "pdf": 0.8953488372093024,
+        "ui_screenshot": 0.5833333333333334,
+        "video": 0.625
+      },
+      "modality_counts": {
+        "code": 128,
+        "image": 26,
+        "pdf": 86,
+        "ui_screenshot": 36,
+        "video": 24
+      }
+    },
+    {
+      "label": "words only, wider page",
+      "top_k": 30,
+      "dense": false,
+      "visual": false,
+      "recall_at_10": 0.855,
+      "mrr": 0.6895710215710216,
+      "hit_at_10": 0.8566666666666667,
+      "wilson_lower": 0.8124994228503402,
+      "p50_seconds": 0.06468957299512113,
+      "p95_seconds": 0.1297007056004077,
+      "median_distinct_sources": 6.0,
+      "by_modality": {
+        "code": 0.953125,
+        "image": 0.75,
+        "pdf": 0.9186046511627907,
+        "ui_screenshot": 0.5833333333333334,
+        "video": 0.625
+      },
+      "modality_counts": {
+        "code": 128,
+        "image": 26,
+        "pdf": 86,
+        "ui_screenshot": 36,
+        "video": 24
+      }
+    },
+    {
+      "label": "+ dense leg",
+      "top_k": 10,
+      "dense": true,
+      "visual": false,
+      "recall_at_10": 0.845,
+      "mrr": 0.6632195767195768,
+      "hit_at_10": 0.8466666666666667,
+      "wilson_lower": 0.8015339429418983,
+      "p50_seconds": 0.4729458744986914,
+      "p95_seconds": 0.5775450093562541,
+      "median_distinct_sources": 5.0,
+      "by_modality": {
+        "code": 0.9375,
+        "image": 0.75,
+        "pdf": 0.8837209302325582,
+        "ui_screenshot": 0.6388888888888888,
+        "video": 0.625
+      },
+      "modality_counts": {
+        "code": 128,
+        "image": 26,
+        "pdf": 86,
+        "ui_screenshot": 36,
+        "video": 24
+      }
+    },
+    {
+      "label": "+ dense leg, wider page",
+      "top_k": 30,
+      "dense": true,
+      "visual": false,
+      "recall_at_10": 0.8583333333333333,
+      "mrr": 0.6686637344137344,
+      "hit_at_10": 0.86,
+      "wilson_lower": 0.8161683876490509,
+      "p50_seconds": 0.41367531901050825,
+      "p95_seconds": 0.5258364057008293,
+      "median_distinct_sources": 7.0,
+      "by_modality": {
+        "code": 0.9375,
+        "image": 0.75,
+        "pdf": 0.9186046511627907,
+        "ui_screenshot": 0.6388888888888888,
+        "video": 0.6666666666666666
+      },
+      "modality_counts": {
+        "code": 128,
+        "image": 26,
+        "pdf": 86,
+        "ui_screenshot": 36,
+        "video": 24
+      }
+    },
+    {
+      "label": "+ picture leg",
+      "top_k": 10,
+      "dense": false,
+      "visual": true,
+      "recall_at_10": 0.815,
+      "mrr": 0.5525886243386243,
+      "hit_at_10": 0.8166666666666667,
+      "wilson_lower": 0.7689713823226987,
+      "p50_seconds": 0.13248339149868116,
+      "p95_seconds": 0.2713221473451995,
+      "median_distinct_sources": 8.0,
+      "by_modality": {
+        "code": 0.9296875,
+        "image": 0.75,
+        "pdf": 0.8488372093023255,
+        "ui_screenshot": 0.5277777777777778,
+        "video": 0.5833333333333334
+      },
+      "modality_counts": {
+        "code": 128,
+        "image": 26,
+        "pdf": 86,
+        "ui_screenshot": 36,
+        "video": 24
+      }
+    },
+    {
+      "label": "+ picture leg, wider page",
+      "top_k": 30,
+      "dense": false,
+      "visual": true,
+      "recall_at_10": 0.825,
+      "mrr": 0.5507809648412589,
+      "hit_at_10": 0.8266666666666667,
+      "wilson_lower": 0.7797738592974309,
+      "p50_seconds": 0.16283495400421089,
+      "p95_seconds": 0.2837207488548302,
+      "median_distinct_sources": 20.0,
+      "by_modality": {
+        "code": 0.8984375,
+        "image": 0.75,
+        "pdf": 0.9069767441860465,
+        "ui_screenshot": 0.5,
+        "video": 0.7083333333333334
+      },
+      "modality_counts": {
+        "code": 128,
+        "image": 26,
+        "pdf": 86,
+        "ui_screenshot": 36,
+        "video": 24
+      }
+    }
+  ],
+  "machine": {
+    "system": "Linux",
+    "release": "6.18.40.1-microsoft-standard-WSL2",
+    "cpus": 32,
+    "threads_per_query": 4,
+    "load_average_1_5_15": [
+      25.51,
+      9.62,
+      5.7
+    ]
+  }
+}
\ No newline at end of file
diff --git a/bench/reports/quant.json b/bench/reports/quant.json
new file mode 100644
index 0000000..3abd721
--- /dev/null
+++ b/bench/reports/quant.json
@@ -0,0 +1,110 @@
+{
+  "commit": "cd9851a12c000199ce5c211f937ef6c1e0138efa",
+  "vectors": 165973,
+  "dimensions": 384,
+  "probes": 200,
+  "top_k": 10,
+  "recall_note": "probes are drawn from the corpus itself, so these arms are compared with each other, not quoted as retrieval quality; a hit is any returned atom whose true cosine is at least the 10-th best, because byte-identical atoms make the id set ambiguous",
+  "bytes_per_vector": {
+    "float32": 1536,
+    "int8": 384,
+    "bit": 48
+  },
+  "arms": [
+    {
+      "arm": "sqlite-vec float",
+      "queries": 200,
+      "p50_ms": 41.2,
+      "p95_ms": 55.4,
+      "recall_at_10": 1.0
+    },
+    {
+      "arm": "sqlite-vec float + holder filter",
+      "queries": 200,
+      "p50_ms": 29.8,
+      "p95_ms": 30.7,
+      "recall_at_10": 0.599
+    },
+    {
+      "arm": "sqlite-vec int8",
+      "queries": 200,
+      "p50_ms": 35.5,
+      "p95_ms": 42.2,
+      "recall_at_10": 0.977
+    },
+    {
+      "arm": "sqlite-vec int8 + holder filter",
+      "queries": 200,
+      "p50_ms": 22.3,
+      "p95_ms": 28.1,
+      "recall_at_10": 0.595
+    },
+    {
+      "arm": "sqlite-vec bit",
+      "queries": 200,
+      "p50_ms": 2.6,
+      "p95_ms": 2.8,
+      "recall_at_10": 0.728
+    },
+    {
+      "arm": "sqlite-vec bit + holder filter",
+      "queries": 200,
+      "p50_ms": 4.1,
+      "p95_ms": 4.3,
+      "recall_at_10": 0.513
+    },
+    {
+      "arm": "numpy float32 scan (the truth)",
+      "queries": 200,
+      "p50_ms": 8.0,
+      "p95_ms": 9.6,
+      "recall_at_10": 1.0
+    },
+    {
+      "arm": "numpy hamming (sign bit)",
+      "queries": 200,
+      "p50_ms": 18.9,
+      "p95_ms": 19.8,
+      "recall_at_10": 0.011
+    },
+    {
+      "arm": "numpy float32 first 192 dims",
+      "queries": 50,
+      "p50_ms": 7.5,
+      "p95_ms": 8.2,
+      "recall_at_10": 0.722
+    },
+    {
+      "arm": "numpy float32 first 128 dims",
+      "queries": 50,
+      "p50_ms": 7.9,
+      "p95_ms": 8.9,
+      "recall_at_10": 0.57
+    },
+    {
+      "arm": "numpy float32 first 64 dims",
+      "queries": 50,
+      "p50_ms": 4.0,
+      "p95_ms": 8.3,
+      "recall_at_10": 0.43
+    },
+    {
+      "arm": "bit coarse@200 -> float32 rerank in numpy",
+      "queries": 200,
+      "p50_ms": 23.7,
+      "p95_ms": 24.7,
+      "recall_at_10": 0.983
+    }
+  ],
+  "machine": {
+    "system": "Linux",
+    "release": "6.18.40.1-microsoft-standard-WSL2",
+    "cpus": 32,
+    "threads_per_query": 4,
+    "load_average_1_5_15": [
+      2.46,
+      4.17,
+      4.58
+    ]
+  }
+}
\ No newline at end of file
diff --git a/bench/reports/scale.json b/bench/reports/scale.json
index 16a7c0b..49f29c9 100644
--- a/bench/reports/scale.json
+++ b/bench/reports/scale.json
@@ -1,7 +1,10 @@
 {
-  "database": "bench/runs/baseline/data/ely_eye.sqlite",
+  "commit": "dff5977ab638896cc8685c78ee41e5195fc0fbf2",
+  "database": "bench/runs/scale/data/ely_eye.sqlite",
   "atoms": 165973,
-  "migration_seconds": 0.00044073900062358007,
+  "copy_seconds": 4.996028091998596,
+  "schema_version": 8,
+  "migration_seconds": 0.03624351899634348,
   "dense_embeddings_enabled": false,
   "probes": 300,
   "probe_modalities": [
@@ -13,10 +16,10 @@
   ],
   "latency": {
     "queries": 300,
-    "mean": 0.06524801251696771,
-    "p50": 0.057020749500225065,
-    "p95": 0.13832052519683205,
-    "max": 0.5395459059946006
+    "mean": 0.0888331904130852,
+    "p50": 0.07169728449662216,
+    "p95": 0.21773518459958724,
+    "max": 0.7804536300027394
   },
   "machine": {
     "system": "Linux",
@@ -24,9 +27,9 @@
     "cpus": 32,
     "threads_per_query": 4,
     "load_average_1_5_15": [
-      1.77,
-      1.62,
-      1.64
+      2.31,
+      3.72,
+      7.9
     ]
   }
 }
\ No newline at end of file
diff --git a/bench/reports/term_sweep.json b/bench/reports/term_sweep.json
new file mode 100644
index 0000000..e9f07dd
--- /dev/null
+++ b/bench/reports/term_sweep.json
@@ -0,0 +1,81 @@
+{
+  "commit": "dff5977ab638896cc8685c78ee41e5195fc0fbf2",
+  "store": "bench/runs/term_sweep/data/ely_eye.sqlite",
+  "copy_seconds": 5.062254256001324,
+  "probes": 173,
+  "shipped_fraction": 0.05,
+  "peak_fraction": 0.05,
+  "arms": [
+    {
+      "fraction": 0.2,
+      "shipped": false,
+      "recall_at_10": 0.791907514450867,
+      "mrr": 0.6286195981282686,
+      "hit_at_10": 0.791907514450867,
+      "p50_seconds": 0.4202946660007001,
+      "p95_seconds": 1.7041233958021622,
+      "median_distinct_sources": 6
+    },
+    {
+      "fraction": 0.1,
+      "shipped": false,
+      "recall_at_10": 0.791907514450867,
+      "mrr": 0.6315097715386733,
+      "hit_at_10": 0.791907514450867,
+      "p50_seconds": 0.46752902700245613,
+      "p95_seconds": 1.7474705398024528,
+      "median_distinct_sources": 6
+    },
+    {
+      "fraction": 0.05,
+      "shipped": true,
+      "recall_at_10": 0.8034682080924855,
+      "mrr": 0.6520300027525461,
+      "hit_at_10": 0.8034682080924855,
+      "p50_seconds": 0.069478709003306,
+      "p95_seconds": 0.13147423019836424,
+      "median_distinct_sources": 5
+    },
+    {
+      "fraction": 0.02,
+      "shipped": false,
+      "recall_at_10": 0.791907514450867,
+      "mrr": 0.6311244150839527,
+      "hit_at_10": 0.791907514450867,
+      "p50_seconds": 0.03335734199936269,
+      "p95_seconds": 0.08840757200086956,
+      "median_distinct_sources": 4
+    },
+    {
+      "fraction": 0.01,
+      "shipped": false,
+      "recall_at_10": 0.7745664739884393,
+      "mrr": 0.61696256537297,
+      "hit_at_10": 0.7745664739884393,
+      "p50_seconds": 0.019158394003170542,
+      "p95_seconds": 0.06302069279918217,
+      "median_distinct_sources": 4
+    },
+    {
+      "fraction": 0.005,
+      "shipped": false,
+      "recall_at_10": 0.7398843930635838,
+      "mrr": 0.5633659051289108,
+      "hit_at_10": 0.7398843930635838,
+      "p50_seconds": 0.01079572799790185,
+      "p95_seconds": 0.06986973820021376,
+      "median_distinct_sources": 3
+    }
+  ],
+  "machine": {
+    "system": "Linux",
+    "release": "6.18.40.1-microsoft-standard-WSL2",
+    "cpus": 32,
+    "threads_per_query": 4,
+    "load_average_1_5_15": [
+      2.32,
+      3.87,
+      8.09
+    ]
+  }
+}
\ No newline at end of file