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.
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.
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.
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.
- 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
ZacharyZhang-NY