Round 1 found a real defect: moving code between files dropped three
multi-line docstrings, and what dropped was contract — the rule that
every search filter is one `? IS NULL OR` predicate in a literal
statement, the as_holder sharing rule, the five arms of the ablation.
Round 2 found the line numbers had been read off a tree pi-lens reflowed.
The previous fix quoted migrations.py:248,250, which are a PRAGMA and a
blank line in the committed file. Those two numbers came from a working
tree pi-lens had already reflowed, so the ladder looked nine lines lower
than it is.
Cited from the commit: 181 and 186 define the v2 steps, 239 and 241 call
them, 243 rebuilds FTS for v3. The other six line citations on that
CHECKLIST line were re-checked the same way and hold.
A split moves code; it does not get to rewrite what the code promises. The
first pass collapsed three multi-line docstrings into one-liners and the
line counts in the checklist were measured before that happened.
- store_search.py: search_atoms keeps its filter-shape rule and the
as_holder sharing rule, FtsPage keeps why the cursor is an offset
- bench/run.py: the module docstring says what the five arms are again
- CHECKLIST: 116/341/83 lines, 11 call sites over 7 methods, and the
migration ladder now cites 181/186/248/250 instead of the parent's lines
Repo-wide multi-line docstrings are back to 114, the parent's count.
The 500-line rule and the one-line-comment rule were enforced only from
memory, so they drifted. bench/filesize_check.py now checks the budget on
every tracked source file, and it passes.
Split, with behaviour proven unchanged by comparing each moved function body
against HEAD byte for byte (53 training bodies, 21 benchmark bodies, 0
differences):
db.py 503 -> 396 store_search.py 105
bench/run.py 609 -> 351 query_set.py 80, report.py 211
benchmarks.py 619 -> 424 benchmark_scoring.py 262
training.py 1630 -> 94 training_{common,encode,metrics,datasets,lora,retrieval}
TrainingService stays as a thin facade for the CLI; the facade's parameter
lists are pinned against the module functions by inspect, which is what
catches a leaked self.
Deleted the eight top-level functions no file called, after fixing the scan
that had reported 201 false positives by skipping the defining file. The
v1/v2/v3 sweep found nothing to delete and says so: every remaining version
tag is an on-disk format marker, a migration step, a fixture name, or a
third-party model name.
Collapsed 39 multi-line comment blocks (86 lines) to one line each, keeping
the measured facts and error strings that justify the code.
269 tests pass; ruff clean; mypy 24 errors in 9 files against a 24-in-6
baseline, net zero; pyright 4, the same four HEAD measures.
Measured, not guessed: every top-level def in every tracked .py file was
counted against every other tracked file, including markdown, scripts, Go
and TypeScript. Eight names had zero references anywhere:
cartridges_holding_atom and clear_mounts (memory.py reads each cartridge's
own sources.json, and set_mounts already clears by replacing the set),
gradient_checkpointing_enabled, group_hits_by_source, ingested_paths,
is_finite_number, parse_runtime_json (the runtime answers in prose now, and
the JSON path was never wired back), write_sources_json.
Typer commands and pytest cases were excluded from the count because a
framework calls them by name. The same scan now returns zero.
263 tests pass; repo-wide mypy is still 24 errors in 6 files, unchanged.
p1-6's acceptance names bench/filesize_check, and the file did not exist:
the 500-line rule was enforced by memory. It now runs, and it fails on
training.py 1639, benchmarks.py 619, bench/run.py 609 and db.py 503. That
is the honest state of the tree, not a passing gate invented after the work.
The console work passed independent review on round three: the reviewer
re-ran the smoke, then broke the pager three ways and watched each break go
red - a pager that does nothing, a pager that appends, and a Previous button
that stops restoring.
The E2E waited for the number of `.atom-row` elements to change, but the
search list and the browse list share that class, so the count it watched
was 12 browse rows plus one search hit. A pager that did nothing still
passed. Rows now carry `data-atom` and the two lists carry `data-testid`,
and the test asserts the ordered id list: page one holds exactly PAGE_SIZE
rows, page two shares none of them, page two holds total - PAGE_SIZE rows,
and Previous restores the first list.
Both mutations the reviewer suggested now fail: dropping setCursor and
loadBrowse from nextPage gives "Next page did not swap the page: the pager
is not paging", and appending instead of replacing gives "the next page
repeated 12 of 12 atoms from the first page".
The record also said 130 lines for App.tsx when it is 131, and quoted a
mount refusal the harness had cut off mid-sentence; the refusal's own
remediation ("set ELY_EYE_MODEL_DIR to the model snapshot") is the useful
half, so the slice is 200 now and the quote is the product's full sentence.
9f91ff7 pushed schemas.py from 498 to 534 lines and broke the 500-line rule
the repo enforces on every file. The status family (CacheLayerStatus,
MemoryStatus, RuntimeStatus, SenseStatus, SystemStatus) moves to
status_schemas.py, where it is self-contained: it needs only
TrainingAdapterKind from schemas.py, and schemas.py no longer looks back.
The same commit added three multi-line docstrings (browse.picture,
senses.sense_facts, SenseStatus). Each is now one line; the absent-vs-missing
distinction they carried survives in SenseStatus's own line, and the producer
senses.py still states why the two failures need different sentences.
Measured, not asserted: 263 tests pass; ruff clean on the eight touched
files; repo-wide mypy still 24 errors in 6 files (runtime.py's two are in
that baseline and reproduce on the HEAD copy of the file); pyright reports
the same two pre-existing runtime.py tuple findings on both trees;
multi-line docstrings repo-wide are back to 114, the parent's count.
The store had a write path, a mount path and a search path, and the console
still showed a dashboard of counters. Nothing in the UI could ask what someone
knows, page through atoms, look at a wordless picture, forget one memory, or
mount a cartridge — so the mechanisms built in P0 were invisible.
Backend:
- senses.py: one function decides what this machine can perceive. /api/status
and doctor each computed it before, which is two truths. Picture atoms are now
counted by image_ref rather than by modality, so a screenshot crop from a PDF
counts and a picture whose bytes were deleted stops claiming it can be encoded.
- browse.py: GET /api/atoms pages newest-first through v_atoms_current with
modality/holder/source filters; GET /api/atoms/{id}/image serves the stored
bytes so a picture with no OCR text can be looked at.
- Deleted 8 HTTP routes nobody calls (4 benchmark, research modules, dense index,
visual-contradiction-proof, memory-map). Their functions stay: the CLI and the
proof checks use them. The surface is 30 routes, pinned as a set by a test so a
new unused route fails the suite.
Frontend: App.tsx (517 lines) became a 131-line shell plus seven panels that own
their own fetches, and lib/format.ts. types.ts now carries the memory dimensions
the backend has been sending since schema v2.
Two invented fields were rendering lies: AdapterPanel showed gpu_available and
probe_status, which AdapterLoadReport does not have — every machine looked GPU-less
including this one. The panel now shows only what the server says.
Proof: scripts/ui_smoke.sh starts the API on a fresh temp home, serves the built
UI from the same origin, and drives five interactions in a real browser — search
as somebody in a scenario, page and view a wordless picture, forget, mount, read
adapter states with reasons. It prints the mount refusal verbatim rather than
pretending the mount succeeded.
Gates: 263 pytest passed; ruff and mypy clean on the 8 touched backend files
(repo-wide mypy unchanged at 24 errors / 6 files); pyright 0 errors there;
go build/vet/test/gofmt clean; npm build + eslint clean; ui_smoke and
mcp_write_smoke both PASS.
The reviewer ran things rather than reading my claims: gh run view, pytest,
ruff, cold-cache mypy diffed against the pre-p1-4 tree, pyright, the full go
suite, bash -n on all 15 .sh, pip wheel, sha256sum of LICENSE against gnu.org,
the 22.4GB container answering three URLs, and three mutation tests on the
harness stubs. Round 1 failed 9 checklist numbers, round 2 failed one two-line
docstring, round 3 passed and measured that p1-4 now adds zero multi-line
docstrings repo-wide (114 -> 114).
The reviewer's round 2 verified all nine round-1 corrections by re-measuring
them, then failed the commit on the one thing it added that the repo's own bar
forbids: a two-line docstring on _TinyDense. p1-6.4 still lists "comments to one
line" as open work, so a new commit should not widen it. The 470MB rationale
already lives in CHECKLIST.md.
Also cites the CI run for the commit under review (37744547992 @ 20c0416, also
success) instead of stopping at the previous one.
Nine claims in CHECKLIST.md did not reproduce. All nine were prose, not
product, and all nine are now the measured value:
- test files: 19 (18 test_*.py + conftest.py), not 24
- suite size: 253 passed (239 at 922d012), not 252
- build context: 81G on this machine (bench/runs 60.2GB, bench/corpus 20.7GB,
.venv 2.5GB), not 19GB
- profiles.py probes: linux_package_versions catches the 3-tuple because
json.loads needs ValueError; runtime_launcher_probe and gpu_available catch
the 2-tuple. The old sentence claimed one tuple for all three.
- training.py: the CUDA raise is at :1271, not :1272
- cli.py is 490 lines at this commit, not 488
- the bf16 falsifier said "a 4090 that does not support bf16". A 4090 is Ada
(compute 8.9) and does support BF16; the test fabricates the flag. Naming the
card was exactly the error this commit removes, so the sentence now names the
flag.
- the backup digest is a fingerprint of one store snapshot: hashing is
deterministic per state, but the store was rewritten after the commit, so the
current digest differs (498b0cd9...). The record now pins mtime, size and row
counts next to the digest.
- wall times moved between runs (backup 4.0s vs 2.24s, restore 2.8s vs 5.65s),
so they are reported as a range with the reason instead of a single number.
CI failed four tests with "SystemExit: bench/models/paraphrase-multilingual-
MiniLM-L12-v2: model not downloaded". The claim those tests make is that the
joint ablation pass ranks exactly like the per-arm pass and that three arms
needing BM25 score the corpus once. That is a claim about the harness, not
about embedding quality, so it should not require a 470MB download to check.
The tests now inject a deterministic 384-dimensional stand-in dense model and
a stand-in cross-encoder at the seam both paths share. The real models still
carry every measurement in bench/reports; nothing measured changed.
Proven by the condition that broke: with both model directories moved out of
the tree, the whole suite passes (253 passed).
CI was a workflow that could not run its own tests: pytest is not a
dependency of anything, so a fresh clone fails with "No module named
pytest". Test tooling now lives in the backend's dev extra, so CI and a
new contributor install the same tools, and ruff is pinned there instead
of drifting as a bare version in a workflow step.
backup, restore and doctor move the store. Backup uses SQLite's own
online API so a running server is safe, and the manifest records the
schema version, row counts, sha256 and size read back from the copy.
Restore verifies the digest, integrity_check, required tables and schema
age before it touches the live file, and keeps what it replaces as
.before-restore.
Doctor separates broken from degraded. On the 906 MB baseline store
(165,973 atoms) it reports no problems and one true warning: 21,362
picture atoms with no CLIP snapshot configured, so those atoms are
findable only by the text already on them. The first version called that
store healthy because it only complained about paths that were set and
missing, never about a sense the store needs and never had.
AGPL-3.0-only, because PyMuPDF is dual-licensed AGPL-or-commercial and
the repository is public. A permissive license needs the Artifex
commercial license bought first; the README says so rather than hiding it.
Gates: 253 tests pass; ruff clean repo-wide (17 errors at HEAD); mypy
unchanged from HEAD on touched files; pyright 0 errors on the new files;
backup 4.0s and restore 2.8s on real data with counts verified equal.
The product could only reach a GPU through one hard-coded route: `wsl.exe -d
Ubuntu`, a device named "RTX 4090", and launch scripts that existed only as
PowerShell. On this machine `WSL_DISTRO_NAME` is already set, so the local
shell is Linux and the wsl.exe hop was unnecessary; on any other Linux host
it is impossible. Every SGLang, vLLM and KTransformers profile was reported
"missing" here even with a working 4090.
profiles.py now asks the host what it is instead of assuming: `uses_wsl()`
is false inside WSL and only true when wsl.exe is actually on PATH,
`host_shell()` runs `bash -lc` directly or through `wsl.exe -d
settings.wsl_distro`, `gpu_available()` replaced `wsl_gpu_available()`, and
`launch_script()` picks the .sh twin on a Linux host and the .ps1 on a
Windows one. The schema field `wsl_gpu` became `gpu_available` because the
old name lied on Linux.
Training no longer fails on a card name. `cuda_bf16_training()` gates on
cuda_available plus bf16_supported, which the training environment already
records, so an RTX 5090 or a 4080 passes and a non-BF16 4090 still fails.
Fourteen .sh twins cover the six launch profiles plus install, verify,
serve, download and finetune; the six WSL-hopping .ps1 files read
ELY_EYE_WSL_DISTRO instead of Ubuntu. PowerShell parsing is not verified
here because no pwsh exists on this host.
ci.yml runs pytest, ruff, go build/vet/test/gofmt, the Vite build and a
docker build-plus-curl on every push. To make that lint gate mean anything,
the 17 pre-existing ruff findings across backend/src are cleared: 14
mechanical fixes plus three probe handlers that now name the failure
families they absorb (OSError, SubprocessError, ValueError) instead of
catching Exception.
Verification: pytest 239 passed; ruff check backend/src bench clean (17 at
HEAD); mypy on the 12 touched files 14 errors, identical at HEAD; pyright on
the same files 8 errors, identical at HEAD; bash -n clean on all 15 .sh.
Without a .dockerignore a build from the developer machine sends the 19 GB
corpus, the virtualenv and node_modules to the daemon; the Dockerfile reads
none of them.
The Dockerfile copied PRD.md, which is not in the repository, so a clean
clone could not build at all. It also installed node and npm from Ubuntu 24
(node 18) to build a Vite 8 front end that needs node 20.19 or newer, and it
built the interface into a directory nothing served.
The build is now two stages: node:22 builds the interface, the CUDA runtime
image installs the package and carries the built files. The API serves them
when ELY_EYE_UI_DIR points at a directory that exists, and refuses to start
when it is set but empty, naming the build command. The front end already
calls /api/... on its own origin, so same-origin serving needs no client
change.
The reviewer ran its own probes and mutation tests rather than reading the
prose, and confirmed the repo rules. One of its observations, that mount
state is per-connection, I disproved by measurement: mounts live in a table
and a second Database object on the same home reads them back, so the test
asserts an empty mount list only because that test mounts nothing.
Round three of the adapter review found two leftovers from the previous fix.
With three or more live runtimes the reason read "another live model still
holds adapter-a and adapter-b", a singular subject attributing several
adapters to one model, when each runtime attaches one adapter per kind. The
subject now agrees in number with what is held.
LoadRecord still stored its own copy of the manifest sha256, which is the
same second source of truth this work removed for kind, adapter_id and path:
the only constructor copied it, and nothing stopped a caller from passing a
hash that disagreed with the source handed in. It is a property now, so
runtime.py keeps reading record.sha256 and there is one value.
Also recorded, measured by the reviewer and deliberately not treated as
defects: a row for a kind with nothing on disk now reports base_model as null
rather than the runtime's model id, since that row describes no adapter at
all; the same adapter id can appear in both a loaded row and a shadowed row
when it is attached while another copy wins the mount, and both rows are
true; and when the attached adapter is deleted from disk the row says missing
with null identity instead of claiming a gone model.
Three tests carry this: the three-runtime case asserts the plural wording, and
both fixes were mutation-checked (dropping the plural branch fails two tests,
restoring the stored hash fails the generation test).
pytest -q: 228 passed. ruff clean. mypy cold cache on the three touched source
files: 2 errors, identical to the stashed HEAD tree, net zero. pyright: 2
errors, both the known qwen_vl_utils tuple artifact.
The report row contradicted itself. The reason came from the attachment
record while path, adapter_id, base_model, origin and cartridge_id came from
whatever resolution preferred right now. Reproduced without any stubbing: a
live model holds adapter X, training writes a newer adapter Y, and the
endpoint then answers state=loaded adapter_id=adapter-Y while explaining
"attached to the live model from .../x-attached". A JSON consumer reads the
identity columns as the loaded adapter and gets the wrong one.
The same root cause had a second symptom. Attachments were keyed by kind with
last-writer-wins, so with two live runtimes one unload made the row claim
"that model is gone" while the other runtime's model was still alive holding
its adapter.
LoadRecord now carries the AdapterSource it came from, so a row built from a
record has exactly one identity, and the registry is a list of attachments
each holding its own weak reference: loaded means some live model still holds
an adapter, and when more than one does the reason names the other.
clear_loads had no production caller once unload stopped erasing the process
record, so it is gone rather than kept as a test-only entry point.
Two tests pin both symptoms, and both were mutation-checked: restoring the
identity to the resolved source fails the first, and letting
live_attachments ignore liveness fails the second and the unload test.
pytest -q: 227 passed. ruff clean. mypy cold cache on the three touched
source files: 2 errors, byte-identical to the same command on a stashed HEAD
tree, net zero. pyright: 2 errors, both the known qwen_vl_utils tuple
artifact, unchanged from HEAD.
Independent review of the adapter resolution work found six defects, all
real and all fixed here.
A kind whose only candidates were rejected had no winning path, so its row
carried path=None and the rejected candidate fell into the shadow loop: the
report said "another adapter of this kind wins for the current mount" while
nothing won, and the real deferred reason was buried. A shadow row now
requires a winner and names it.
"loaded" was backed by a process-global record, so a fresh runtime with no
model still claimed a live adapter. The record is now held by the instance
that attached it, and the report keeps a weak reference: while the model
lives the row says loaded, once it is gone the row says available and
explains that the model is gone.
The sort key had lost the manifest's cartridge tag, which changed which
adapter loads on any machine holding a tagged library copy, and neither the
commit message nor the checklist said so. The tag is back as the middle key.
Proof checks resolved through the library while generation resolved through
the mount, so one proof payload could name two different copies. Both now
resolve the way generation does.
loaded_at was written and never read, so it is gone. The checklist claim that
peft is lazily imported in one place was true only for generation and is now
scoped that way.
The four generation paths and status() had no test coverage, so "no function
was lost" rested on reading. One test now walks the transformers prompt path
end to end with stub weights and asserts the generation answer carries the
adapter it attached.
pytest -q: 225 passed. ruff clean. mypy on the three touched source files:
2 errors, the identical 2 on a stashed HEAD tree, net zero. pyright: 2
errors, both the known qwen_vl_utils tuple artifact, unchanged from HEAD.
runtime.py resolved adapters at 7 call sites and only ever looked in the
adapter library, so an adapter attached to a cartridge (cartridge.py:230
copies it into <cartridge>/adapters/{kind}/) was never loaded by anything.
Resolution now lives in adapters.py: it reads the mounted cartridge set,
prefers a mounted cartridge's copy over the library, and answers every one
of the six kinds with a state the facts support — loaded, available,
deferred (base_model mismatch or missing weights, both models named in the
reason), missing — plus a shadowed row for each adapter that lost the
choice. A mounted cartridge is the scenario selection; neither manifest
carries a scenario field, so none was invented.
Two bugs the new tests caught: the preference sort lost reverse=True, so a
library adapter beat a mounted cartridge's copy, and the load branch had
never been executed by a test. Stub weights now prove peft is asked to
attach, that the returned model is put in eval mode, and that the endpoint
reports loaded until unload clears it.
Faces: GET /api/runtime/adapters, `ely-eye adapters`, and a log line naming
the reason whenever a generation runs without an adapter.
Gates: 222 passed; ruff clean; mypy cold cache on the 7 touched files gives
the same 2 errors as HEAD's runtime.py, and pyright gives the same 2 as
HEAD — both are qwen_vl_utils typing artifacts (process_vision_info returns
2 or 3 values depending on return_video_kwargs; the product takes 2).
The reviewer ran the harness and the gates itself rather than trusting the
prose: round one failed on 11 defects, round two on 4, round three passed
after re-running bench.asr_check in a fresh process, re-checking the stamp
chain and matching 18 checklist assertions.
The report is regenerated after 10a39c5 so its stamp names the code it
describes. Speed is quoted as a range in the checklist because transcribe
seconds move with machine load (3.26s then 3.12s on this box) while WER,
denominators and word counts do not move at all.
Numbers: 258.18s of audio, WER 0.242 over 533 reference words, 80 audio atoms
and 563 word timestamps read back from the store, 0 out of order and 0 before
their own segment measured on the stored words.
A second review found three numbers left behind when the report was
regenerated with pinned decoding:
- the transcriber docstring claimed 1.7 s for 88.9 s of speech; the report
says 1.39 s, and 1.7 came from the unpinned run. The docstring now names no
number, so it cannot go stale, and it stops calling container seconds speech
with voice activity detection off.
- the checklist still quoted 3.55 s and 61.6x / 73.0x / 97.7x; the frozen
report says 3.26 s and 63.9x / 82.4x / 112.2x.
- the checklist still said 31 speech segments for one mp4; with greedy decoding
that video yields 38, which is where the store's 80 atoms come from
(38 + 39 + 3).
The report also credited the store with timing invariants that were measured on
the model's answer. The harness now recomputes them from the words as stored
(out_of_order_in_store, before_their_segment_in_store) and says so.
Verification: 212 passed, ruff clean, mypy clean on both touched files with a
cold cache, pyright 0 errors.
Generated by .venv/bin/python -m bench.asr_check after 709104e, so the stamp
names the code under measurement instead of its parent. Two passes agreed:
WER 0.242 both times, transcripts byte-identical.
A review of 5289cb4 found the ASR report was not honest arithmetic:
- "audio s" was the last segment's end, not the media length. The real
container durations are 88.93 / 109.18 / 60.07 s, so the report understated
the audio by 48 s and every speed cell was wrong.
- Decoding was unpinned, so a rerun was a new draw: WER moved 0.242 to 0.262
and one video moved 0.278 to 0.583. Greedy single-temperature decoding with
no carry-over now answers the same way twice, and the harness runs the pass
twice to show it.
- The report claimed its WER was an upper bound. It is not a bound in either
direction: a caption that drops words also hides words the model dropped.
The denominators (201 / 296 / 36 reference words) are printed instead.
- "66 speech atoms" was bench-side arithmetic. The store now says 80 audio
atoms carrying 563 word timestamps, read back after a real ingest.
Same silent-skip bug in a second place: a text file over max_text_file_bytes
returned an empty batch with no reason. It raises FileTooLarge with its size,
and a walk records that refusal like any other.
Also fixed from the same review: the Go search tool never published the audio
modality, cli_senses carried a second json_dump, the no-audio-track branch had
a test that fed it markdown, _seconds was an empty wrapper, and the docstring
quoted a speed no report measured.
Gate root cause: bench/tests imports the bench package by name with no root
conftest, so bare pytest could not collect it at all. pythonpath in pyproject
fixes both launch styles; 212 tests pass either way.
Verification: 212 passed (bare pytest and python -m pytest), ruff clean,
mypy clean on the six touched files with a cold cache, repo-wide mypy 24/6
unchanged from HEAD, pyright 0 errors on the touched files, go build/vet/test
and gofmt clean.
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.
The product's own search answered in p50 20.16s on 165,973 atoms. The SQL
rewrite brought that to 0.68s; this brings it to 57ms and says why the rest was
there.
The cost was not SQL. An OR MATCH scores every row it touches, and a question
carries words the whole library carries: `"parsing"* OR "error"* OR "in"* OR
"the"* OR "pdf"* OR "converter"*` matched 127,145 atoms and took 305ms, while a
CJK bigram phrase query with 32 matches took 1.0ms. Latency tracked match count.
- text_index.rare_terms asks the index which of its own words are noise: count
each term, drop the ones over 5% of the library, keep the rarest few when all
of them are common. No stop-word list; a corpus of PDFs and a corpus of shell
history get different answers. 0.05 is swept, not chosen — on n=173
recall@10 peaks there (0.899 / p50 58.6ms) against 0.893 / 112.9ms at 0.20
and 0.844 / 13.4ms at 0.01.
- The bound is a cost, not a claim about rarity: `cap = max(int(total*fraction)+1, 2)`,
so a word matching one atom in eleven is kept — scoring one candidate is free.
A capped count cannot rank words that all saturate, so those few are counted
exactly.
- Schema v6 adds `term_df`: a word is counted once and the next question pays
nothing. Every atom write clears it, so a stored count cannot outlive the
library it described. Cold p50 73ms, warm 63ms, 1,324 terms cached.
- The denominator is `count(*) FROM atoms` (0.01ms) rather than
`count(*) FROM atom_fts` (37ms cold): FTS rows are written and deleted with
their atoms, so the two counts move together and only one of them is free.
SCALE.md, idle machine (load 1.76, 32 cores, 4 threads per query):
165,973 atoms — 1.66x the contract's 100k — 300 probes, p50 57ms, p95 138ms.
Against the frozen baseline of 20.16s that is 354x. Holder-scoped (n=173):
recall@10 0.870, MRR 0.740, p50 62ms.
Verification: pytest backend/tests bench/tests -q -> 173 passed (5 new
text_index tests: the common word is dropped and the rare one still answers, an
all-common query keeps its rarest words in order, a one-word query is never
filtered, an empty library keeps everything, and a write voids the counts);
ruff clean; mypy fresh cache clean; pyright 0 errors on the touched files.
The frozen baseline claimed the product's top-10 shared 0.0% with the fast
BM25 path. That was a harness bug: gold names a corpus source
(mmlongbench:PH_2016.06.08_Economy-Final.pdf) while the store names a content
hash (PH_2016_06_08_Economy_Final_10e57152f44d1a2b), so the two id spaces never
met and the right document, ranked first, scored as a miss.
- bench/systems.py: `_corpus_ids` joins store sources through the
`source_map()` the counterpart already uses, and refuses a store whose
sources are not corpus files instead of silently scoring zero.
- bench/systems.py: `live_product_rankings(..., as_holders=...)` asks the
product as the query's own holder, which is how the counterpart is scored.
- bench/run.py: the live section now reports recall@10 / MRR / hit@10 with a
95% lower bound, and probes 300 queries instead of 40 — the SQL path answers
in well under a second, so the old cap was a cost estimate that expired.
- bench/run.py: `--name CURRENT` writes a post-P0 report beside the frozen
BASELINE without touching it, stamped with the commit and the store schema
version the pass started on (the live pass migrates the store mid-run, so a
plain stamp would be a lie).
- bench/stamp_holders.py: the pre-P0 store holds 165,973 memories nobody owns.
The corpus index states the owner, so holders, scenarios and event times are
stamped in place — 165,973 atoms across 50,278 sources, 0 left unowned.
Numbers, same machine, load 1.0-1.7:
- CURRENT (post-P0, schema v5): no_rerank recall@10 0.565 p50 0.643s;
fusion+rerank 0.453 p50 7.000s; graph 0.001.
- Product as its holder: recall@10 0.870, MRR 0.740, n=173.
- Counterpart: mem0_flat holder scope 0.866, whole corpus 0.412; the holder
boundary is worth +0.454 with an identical ranker and identical items.
Verification: pytest backend/tests bench/tests -q -> 173 passed; ruff and mypy
(fresh cache) clean on every touched file.
The suite had fifteen checks that could not lose. The visual HashHop arena
painted a cyan line and a white ring on the answer slot, then asked the model
to find the answer by looking; the collector re-rolled puzzles until the
running rate crossed its target; the cache check passed if any layer had ever
recorded an entry; the repository check passed on a call graph built in memory
without touching the store; the research-module check asserted non-empty lists
of SHA256-derived placeholder vectors and so could not fail.
Each check now answers for one claim and reports a verdict the reader can act
on: passed, failed, deferred, or removed. A deferred row names what is missing
and the command that would settle it; a removed row says why the old claim was
withdrawn. The suite verdict is failed > deferred > passed, so a claim that
never ran holds the whole report back from saying "proven".
- evals.py (2427 lines) becomes the proofs/ package; largest module 445 lines.
- proofs/__main__.py deleted: it duplicated PRDProofSuite.latest() with a
different glob. proofs/table.py renders; latest-proof-suite is the one face
and exits 1 on a failed verdict.
- The arena paints nothing. The puzzle id is printed on its tile instead,
because removing the ink without it removes the question.
- HashHop runs a fixed 8 puzzles per depth instead of re-rolling to target.
- The contradiction corpus moves from code to contradiction_scenarios.json;
the check stays deferred while the corpus is the one this repo authored.
- The cache check compiles a nonce question over a real atom twice and reads
the miss to hit movement; an empty store fails instead of agreeing by luck.
- The repository check requires rows in the store; the research-module check is
removed.
Two product bugs surfaced on the way:
- text_index.fts_query joined Latin terms with a space, which FTS5 reads as
AND, so a question longer than its answer retrieved nothing. Terms are ORed
and bm25 rank orders the candidates.
- profiles.windows_path_to_wsl prefixed /mnt/ onto an already-POSIX path with
an empty drive, producing /mnt//home/... and failing every launcher probe.
Verification: 168 passed (pytest backend/tests bench/tests); ruff clean on the
proofs package, cli.py, text_index.py, profiles.py, and both test files; mypy
clean on 16 files with a fresh cache; pyright reports 0 errors on proofs/ and
the test file. Ruff debt elsewhere is unchanged: 19 errors in seven files this
commit does not touch, measured against the same files at HEAD.
A memory system that only answers from its own store is a filing cabinet,
not a partner. The transactive rule the project is named for — not knowing
is fine when you know who does — needed a holder registry, a directory
query, and a referral that never leaks the text it may not read.
- schema v5 adds `holders` (person/agent/service, declared topics, contact).
A holder with memories exists whether or not anybody registered them; the
registry adds reachability, it does not grant existence.
- `who_knows` ranks holders by their own memories and is deliberately NOT
filtered by visibility: "she knows" is the answer. The answer is filtered.
- `ask` searches the asker's visible scope first (hop 0), then walks the
directory with a visited set and a hop budget. Three terminal states:
answered, referred, no_holder_knows. Nobody knowing is a result, not an error.
- Confidence is ordinal, not calibrated: relative strength, coverage, and
declared topics. Every hit carries the raw numbers and the basis lines that
produced the score, so a ranking can be argued with.
- Faces: HTTP (/api/holders, /api/who_knows, /api/ask), CLI (holders,
holder-set, holder-rm, who-knows, ask), MCP (who_knows, ask, list_holders).
Four bugs found by running the thing, not by reading it:
- FTS5 refuses bm25() inside MAX(); the aggregate uses FTS5's own `rank` column.
- A CJK question is longer than the sentence that answers it, so the whole-run
phrase matched nothing. The query now ORs adjacent bigram phrases; adjacency
is kept, equal length is not. Latin path untouched, baseline unaffected.
- The hop budget counted the asker as a hop, so max_hops=1 asked nobody.
- A second, stricter readability test in _answered() disagreed with the SQL
visibility clause and hid a memory the asker was allowed to read. The SQL
clause is the only authority now.
A method named `list` shadowed the builtin inside its own class and made
mypy reject the return annotation; renamed rather than cast around it.
Proof: 155 pytest, 12 Go tests, ruff and mypy clean on touched files, pyright
ledger unchanged at 62, and scripts/mcp_write_smoke.sh drives two principals
on one SQLite file — the husband asks through MCP, gets `referred` with his
wife's contact channel, and her private sentence appears nowhere in the reply.
A cartridge used to be an export: materialize it, hash it, and never let it
change behaviour again. The transactive-memory promise needs the opposite —
mounting the home cartridge must make the work cartridge's memories
unfindable, and appending to a cartridge must make a new memory findable
through it.
Cartridge scope (schema v2 -> v3):
- cartridge_atoms (which atoms are in which cartridge) and cartridge_mounts
(which cartridges this machine has mounted now); both rebuilt on reopen,
because a mount is a machine-local state, not a fact about the data.
- mount / switch / unmount / append refuse a cartridge that was built for a
different runtime, naming every differing field instead of a boolean.
- append rewrites atoms.parquet and sparse_index.bm25.zst, recomputes DNA and
the KV fingerprint, and keeps the cartridges row in one SQLite transaction;
the files are written first so a failed export cannot leave a half-cartridge
in the database.
- RetrievalService.search() now ranks inside the mounted scope by default, and
an explicit cartridge_ids filter overrides the mount. Nothing mounted means
the whole library, so an empty store behaves as it did before.
- forget() removes the forgotten atoms from every cartridge's membership:
forgetting must not leave a memory retrievable through a cartridge.
KV fingerprint (kv_fingerprint.py, stored in the manifest):
- model / tokenizer / chat-template hashes, the RoPE block (factor,
original_max_position_embeddings, mrope_interleaved, mrope_section,
partial_rotary_factor), the adapter hash, the context length, the engine
version, and the fingerprint format version.
- A snapshot we cannot read is recorded as unverifiable and listed in
unverifiable_fields; a blind machine refuses to mount rather than pretending
the runtime matches, because a wrong RoPE factor silently corrupts retrieval.
Two defects found by running the new tests, not by reading:
- atoms.parquet could not export a memory at all: pyarrow refuses a struct with
no child field, so metadata={} failed the whole table. The six JSON columns
are now written as JSON text and decoded by the cartridge reader.
- materialize() inserted cartridge membership before the cartridges row existed,
so the foreign key rejected every cartridge.
api.py crossed 500 lines, so the cartridge and mount routes moved to
api_cartridges.py. That module must not use `from __future__ import
annotations`: FastAPI then resolves Depends(settings_dep) against module
globals, where the injected dependency is only a parameter of register(), and
every route starts demanding settings as a query parameter.
Verification: 138 pytest passed (26 new: 10 fingerprint, 16 scope), Go
build/vet/gofmt/test clean, and scripts/mcp_write_smoke.sh now proves the whole
loop across two processes - mount narrows search, a memory written after the
upload is invisible until append_to_cartridge, unmount restores the library,
and rewriting the RoPE factor makes mount_cartridge refuse with "rope: factor:
cartridge 4.0 vs local 2.0". ruff clean on every touched file (the 20 remaining
package errors are in files this change did not touch; HEAD had 25). mypy: one
error, the pre-existing np.savez stub artifact at cartridge_assets.py:329
(HEAD:320). Repo pyright: 62 errors, unchanged from the p0-3 ledger.
The product path pulled every atom into Python and rebuilt a BM25Okapi
index per query: p50 20.16s over 165,973 atoms. It now runs one SQLite
statement with LIMIT/OFFSET.
- text_index.py: one normalizer for both sides of the index. CJK runs are
split per character (unicode61 cannot segment them), camelCase and
separators are split so "search atoms" finds SearchAtoms, latin tokens
get a prefix match. Rows written before this existed hold raw text in
atom_fts, so schema v3 rebuilds the index on upgrade.
- retrieval.py: filters (holder / scenario / source / modality / since /
until / min_trust), a base64 cursor, and reciprocal rank fusion in place
of the 0.55/0.35/0.10 blend whose graph term was always zero.
- An empty result is {status: "no_match"}, not a bare array.
- Go: search_atoms and compile_context rank through POST /api/search. The
second Go tokenizer is deleted; two implementations could drift, which
is what the MCP/HTTP parity requirement forbids.
- db.py passed 500 lines, so the row codec moved to row_codec.py.
- Tooling: installed the repo's own pyright. pyrightconfig.json had no
exclude and swept the downloaded corpus (1171 errors); with excludes the
count is 62, all pre-existing (evals 48, profiles 4, training 4,
ingestion 3, runtime 2, cartridge_assets 1). backend/pyproject.toml
carried a second [tool.pyright] that shadowed the root config; deleted.
- bench/scale_check.py measures the product search at corpus size. The
100k-atom p50 number is not written until it is measured on an idle
machine.
Verification: 112 pytest passed; go build/vet/test clean, gofmt clean;
ruff clean on every touched file; mypy clean on the 14 touched Python
files; repo pyright 62 errors, unchanged per-file ledger.
remember / supersede / forget now reach the one Python writer from a
terminal, from MCP, and from HTTP. The server, not the caller, decides
who owns a memory.
- cli: remember, supersede, forget. --holder falls back to
settings.default_holder until the holder registry lands. Timestamps are
parsed once with one clear message.
- ingestion: import pymupdf as fitz. The deprecated fitz import printed a
warning to stdout, so every CLI command's JSON answer was unparseable.
- mcp: remember, supersede_memory and forget_memory forward to the Python
writer over HTTP (ELY_EYE_API, default http://127.0.0.1:8765). SQLite
stays read-only to the Go side, so the write rules live in one place.
- mcp reads follow schema v2: atoms.time became ingested_at, and reads go
through v_atoms_current, so a superseded memory stops being findable.
holder, origin, scenario and event_time now show up in fetch_atom and
search_atoms.
- scripts/mcp_write_smoke.sh proves the loop end to end: remember, search,
supersede, forget, then not found.
Evidence: 92 pytest passed; go build, go vet and go test clean; mypy clean
on the 10 touched source files with a fresh cache; ruff clean except a
pre-existing B008 at cli.py:292, identical at HEAD.
An EvidenceAtom could say what was remembered and nothing else: not whose memory it
is, not where it came from, not when it was true, not in what situation it may be
used. The baseline measured the cost of that: the same ranker scores recall@10 0.412
over the whole corpus and 0.866 inside one holder's memory, so the missing holder
column is worth more than any ranking change in the ablation.
- schemas.py: MemoryOrigin, TimePrecision, Visibility; EvidenceAtom gains holder_id,
origin, scenario, event_time / event_time_end / event_time_precision /
event_time_source, valid_from / valid_to, supersedes, visibility, retention_until.
`time` is renamed `ingested_at` — ingestion time gets one name, not two.
A validator refuses valid_to before valid_from, event_time_end before event_time,
and an atom that supersedes itself.
- migrations.py: schema v2 with PRAGMA user_version. A first-version database is
upgraded in place (ALTER + RENAME COLUMN), rows intact, and a database newer than
this build refuses to open instead of being silently downgraded.
- v_atoms_current hides memories that are closed, lapsed or not yet begun; unknown
bounds read as visible. Time travel is db.atoms_as_of(moment, holder) because a
SQLite view cannot take a parameter.
- evaluation_schemas.py: the proof and benchmark report shapes move out of schemas.py
to keep it under the file-size limit; importers follow.
- backend/tests/test_memory_schema.py: 8 tests, including the v1 -> v2 upgrade and the
validity timeline.
Verification of record: pytest backend/tests bench/tests -q -> 70 passed; ruff check on
every touched file -> all checks passed; mypy (fresh cache) on db.py, migrations.py,
schemas.py, evaluation_schemas.py and backend/tests -> no issues. evals.py carries 40
pre-existing mypy errors at HEAD and 40 after this change (measured on both copies);
its pyright findings are the same debt, and p0-6 rewrites that file. `peft` and
`pyarrow.parquet` "could not be resolved" is the documented pyright artifact: pi-lens
runs it with 0 config files, and both import at runtime.
rankings.jsonl stores the seconds each query took, so the report inherited whatever
the machine was doing at the time: the frozen baseline's dense latency included a
per-query model load, and the 50,000-source row moved from 1.315s to 0.809s between
two runs of identical code.
- bench/run.py: --remeasure-latency N re-times a stride sample with warm models and
replaces each arm's latency section, recording the sample size and a note; recall
stays a full-pass number. BASELINE.md prints that note under the table.
- bench/tests/test_joint.py: 62 tests total.
Verification: pytest bench/tests -q -> 62 passed; ruff check bench/ -> all checks
passed; mypy (fresh cache) -> no issues in 25 source files.
Three defects made the external baseline numbers and labels wrong.
- bench/systems.py: _sentence_model rebuilt the model on every query, so dense arm
latency was mostly model loading and the latency-vs-size curve was flat because a
constant dominated it. Models are now cached per process.
- bench/counterpart.py: visible() and search() walked 404k Python objects per query.
Per-item holder/source/time columns are built once and the query path is numpy;
12.5 queries/s instead of 0.7.
- bench/metrics.py: the aggregate key was named wrong_holder_confidence_unanswerable
while it counts top hits that ARE from the asking holder. BASELINE.md read that
backwards. Renamed to top_hit_from_holder_unanswerable and the sentence rewritten.
- bench/run_external.py: the by-modality table read row['answerable'] but aggregate
emits n_answerable, so the writer died after scoring finished. Each store is now
scored twice, [holder scope] and [whole corpus], so the gain from the holder
boundary is separable from the gain from ranking; progress lines every 500 queries.
Verification: pytest bench/tests -q -> 61 passed; ruff check bench/ -> all checks
passed; mypy (fresh cache) -> no issues in 25 source files.
Latency measured while another job hogs the cores is not comparable: the scale curve
moved from 1.315s to 0.809s at 50,000 sources between two runs of the same code.
- bench/systems.py: machine_facts() reports cpus plus the 1/5/15 minute load average.
- bench/run.py, bench/run_external.py: both reports carry it, and BASELINE.md labels
the latency table a ceiling when the machine was busy.
Verification: pytest bench/tests -q -> 60 passed; ruff check bench/ -> all checks
passed; mypy (fresh cache) -> no issues in 25 source files.
The frozen baseline showed the unmodified RetrievalService agreeing with an equivalent
BM25 index on 0% of its top ten results. Reproduced by hand: its top ten atoms came
from one document's pages, so a source-level ranking cannot overlap it.
- bench/systems.py: live_product_rankings also counts distinct sources behind the
product's top-k atoms.
- bench/run.py: scale probes no longer filtered by gold (a source prefix keeps almost
no gold, so the 500/2,000/10,000 rows were silently skipped); scale_arm_names reads
only latency dicts, which is what crashed write_markdown on the note field; the
unanswerable share is computed instead of hard-coded.
- bench/tests/test_joint.py: 60 tests total, including the writer regression.
Verification: pytest bench/tests -q -> 60 passed; ruff check bench/ -> all checks
passed; mypy (fresh cache) -> no issues in 25 source files.
The per-arm runner recomputed BM25 and the dense matmul for every arm, which put the
full ablation at roughly 16 hours and left no way to survive a kill. JointRunner
computes each signal once per query, attributes latency to the components each arm
uses, and checkpoints every query to runs/baseline/rankings.jsonl.
- bench/systems.py: JointRunner + ARM_COMPONENTS signal attribution.
- bench/run.py: joint pass, rankings cache with a run fingerprint, per-query resume,
latency summary, resident-memory progress lines.
- bench/tests/test_joint.py: joint rankings equal the per-arm rankings for all five
arms; the sparse corpus is scored once even with three arms that need it; cache
fingerprint mismatch and malformed rows fail fast.
- bench/reports/EXTERNAL_APPENDIX.md: what a real Mem0/Zep run would cost, and which
published numbers are comparable at all.
Verification: pytest bench/tests -q -> 57 passed; ruff check bench/ -> all checks
passed; mypy (fresh cache) -> no issues in 25 source files.
The merged database names a source by content hash (`11089_4f2a...`) while the
frozen sets name the same file by its corpus id, so every arm scored 0.000 until
load_atoms joined atoms through sources.path. It now returns corpus ids and
refuses an atom it cannot trace to a corpus file.
Also fixes found by running the harness:
- merge rebuilt atom_fts from the deduped atoms table; two attempts on one file
left 288,266 fts rows for 165,973 atoms.
- merge accepts the two honest reasons a corpus file has no product source
(500 byte-identical LongMemEval sessions collapse under content hashing, and
12 photos that ONNX Runtime cannot decode) and reports them.
- --arms used action=extend against a non-empty default, so naming two arms ran
seven.
- the dense cache is keyed by the atom set, so a scale-curve subset cannot be
scored with full-corpus vectors.
- write_markdown crashed on a run that covered only some modalities.
- bench/ingest_baseline.py drives the unmodified product IngestionService over
all 50,789 corpus files in sharded SQLite DBs, then merges them. Thread and
memory ceilings are set explicitly: ONNX Runtime defaulted to every core
(95 threads/worker, load 490) and its CPU arena kept freed pages mapped,
which OOM-killed workers.
- bench/metrics.py: recall@k, MRR, nDCG@10, Wilson lower bound, chance
baseline. No model, no product import, so the scorer cannot grade itself.
- bench/systems.py + bench/run.py: the five ablation arms over the merged DB,
latency percentiles, scale curve, and a top-10 equivalence check against the
live RetrievalService.
- bench/counterpart.py + bench/run_external.py: in-repo Mem0-style flat store
and Zep-style temporal store as the external reference point.
- bench/audit.py: enforces single-attempt scoring, frozen n, a chance row
beside every score, and producer != verifier. It already flags the product's
retry-until-target loop at backend/src/ely_eye/evals.py:2351.
- backend/pyproject.toml: rapidocr-onnxruntime>=1.4.4 was unsatisfiable
(PyPI maxes at 1.2.3); the successor is the renamed rapidocr package.
Ground truth comes only from third-party annotations (MMLongBench-Doc
evidence_page_ids, VisRAG qrels, ScreenSpot-v2 bboxes, Video-MME QA,
LongMemEval answer_session_ids, CodeSearchNet func_path+func_name) and
from document structure; no model judgement is used anywhere.
- bench/corpus/manifest.jsonl: 28 sources with url, retrieved_at, sha256,
annotation_source, license, files[]; verified by bench.manifest_check
- bench/sets/{pdf,image,ui_screenshot,video,code,conversation}.{jsonl} plus
corpus indexes and a manifest that freezes every file's sha256
- 2,928 answerable + 2,380 unanswerable queries over 854 holders; every
query carries scope {holder, scenario, as_of} for transactive routing
- bench/validate_sets.py fails on missing gold, holder mismatch between
gold and query, gold observed after as_of, and manifest digest drift
Local read-only Model Context Protocol server (Go) exposing the .ely_eye memory
store to coding agents such as Codex and Claude Code: nine tools over status,
cartridges, Evidence Atoms, compiled evidence packs, proof suites, and HashHop
proofs. One-line cross-platform install (no Docker), with a prebuilt-binary
release workflow.
Real-models implementation: multimodal ingestion, Context Cartridge memory,
sparse + Qwen embedding retrieval, Cache Fabric, Qwen runtime adapters, verifier
contracts, Visual HashHop proofs, BF16 LoRA fine-tuning, and a local dashboard.
Verified by a 15-check PRD proof suite on the live model.
ZacharyZhang-NY