The dense leg used to be a pair of files next to the database: a full .npy
re-encoded from every atom, re-normalized on every query, and stale the moment
anything was ingested. It is now a vec0 table inside the same SQLite file,
written in the same transaction as the atom it describes, so a memory is
queryable the instant it is remembered.
- vector_index.py maintains the table; the writer inserts, the reader never
rebuilds. A repair scan (ely-eye build-dense-index) catches what a crash hid,
and it is not on the search path.
- Ranking stays in the engine; every boundary (closed, retired, not yet true,
another holder's private memory, the mounted cartridge) is checked once against
the atoms table afterwards. A visibility subquery inside the KNN cost 48.9s on
153,857 vectors; the same check over 200 candidates costs 0.2ms. When the
boundaries reject enough candidates the ask widens once, never twice.
- The Go mirror and the web panel carry the new status fields.
- bench/dense_scale.py and bench/quant_probe.py measure the leg at corpus size;
bench/scale_check.py now works on a copy so the frozen baseline stays frozen.
Measured on the frozen 165,973-atom corpus. bench/reports/DENSE.md, QUANT.md and
SCALE.md carry the numbers and the machine they were taken on.
Gates: 285 pytest, ruff clean, pyright 0 on the touched files, go build/vet/test
and gofmt clean, frontend build and eslint clean. mypy is 25 findings against the
parent's 24: the new one is "Skipping analyzing sqlite_vec: missing library stubs",
the same class as the two existing qwen_vl_utils lines, and the repo keeps no
type: ignore anywhere.
A screenshot with no OCR-able words used to be a memory nothing could
reach. It now has a vector, and a question can be asked with it.
- picture space: Xenova/chinese-clip-vit-base-patch16 fp16 ONNX, 361MB,
sha256 0fad978faa89b85f…, CPU-runnable on the onnxruntime the repo
already ships. Text 76ms, image 29ms/image at batch 8. The ONNX contract
was measured, not assumed: it is the fused contrastive graph, so the
unused tower is fed a placeholder row, and the tokenizer must be
BertTokenizer — a CLIP tokenizer emits ids past the embedding table.
- visual_index.py keeps picture vectors in their own family beside the
text ones, keyed by atom id, and refuses to answer when the map and the
array disagree. retrieval.py stays under 500 lines.
- a search can be asked with a picture, and when the words come up thin
the picture ranking joins the fusion — but only then, so a text query
never pays for the encoder.
- visual tokens are counted at 32x32 patches (migration v7): 21,325 image
atoms went from 5,331 to 34,618 tokens, and the library from 29.9M to
84.2M. The 100M capsule fits this 50,278-file corpus and nothing much
larger — that is what the claim actually means.
- the vision-language describer is written and gated: with no snapshot an
atom records "deferred: no vision-language model snapshot"; a broken
model becomes a note on the atom instead of a dead ingest.
- forget now voids the picture index and the copy this process holds, so
a forgotten picture stops answering from RAM.
Measured, six arms, ScreenSpot-v2 (554 icon crops, 408 whole screenshots),
bench/reports/VISUAL.md:
icon pool picture 0.208 words 0.175 chance 0.018
screenshot pool words 0.384 picture 0.215
screenshot to icon 0.108 — CLIP is a whole-image model, not a grounding
model: it cannot point at one small icon inside a screen.
within one screen MRR 0.908 (recall@10 is 1.000 there only because
chance is 1.000 too, so MRR is the only honest number in that row).
ScreenSpot-v2 instructions are all English, so this run does not test the
bilingual claim and the report says so.
Gates: 199 tests, ruff clean on 17 files, mypy clean on 16 (fresh cache),
the repo's own pyright 0 errors on the 12 files touched.
ZacharyZhang-NY