An audio file used to produce no memory at all: _ingest_file returned an empty
batch for any suffix it did not know, with no log and no reason. It now raises
UnsupportedFile when you name the file and records a SkippedFile when it was a
directory walk, so a refusal is visible either way.
Speech is measured, not promised. Over 210.5 seconds of real Video-MME audio,
faster-whisper-tiny on CPU int8 transcribed at 51x realtime and reached word
error rate 0.242 against community subtitles nobody here wrote (a caption track
drops words a transcript keeps, so that is an upper bound). 66 speech atoms now
carry 550 word-level timestamps, 0 words out of order and 0 starting before
their own segment. The snapshot is 78.2 MB, under the 500 MB cap, and the report
is bench/reports/ASR.md.
Two library facts were measured rather than assumed. faster-whisper's own audio
loader is broken against PyAV 19 (av.open got an unexpected keyword
'metadata_errors') and it declares av with no version bound, so asr.decode_audio
decodes with PyAV directly and hands the model a float32 array instead of
pinning an old av with no cp314 wheel. And av.Error does not exist on PyAV 19 -
the real base is av.error.FFmpegError - so that is the only thing caught and my
own ValueErrors keep their messages.
Files over the 500-line rule were split rather than excused: ingestion.py (748)
into ingestion_common / ingestion_media / ingestion_repository / ingestion, and
cli.py (544) into cli_senses plus cli_common, following the cli_transactive
register pattern. MediaIngest declares _source_record abstract, so a half-wired
host fails at construction instead of returning None.
Verification: 207 tests pass; ruff clean on the 13 touched files; mypy clean on
them, and repo-wide mypy fell from 29 errors to 24 (5 fixed, none added);
pyright reports 0 errors on the touched files.
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