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.
- 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.
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