LEGS.md said "schema v9" while every arm ran on v10. The harness read
PRAGMA user_version straight after copying the store, before the product
had opened it, so it printed the version the copy arrived with. The copy is
migrated on first open, and that is the schema the retrieval service ran on.
The version is now read after the arms, and the report line says both: the
copy arrives at v9, the product migrates it when it opens, every arm ran on
v10. A test pins the sentence so the two versions cannot collapse back into
one number.
Gates: bench/tests 68 passed; ruff clean; mypy clean on the three files.
A memory used to have one state: present. A session could not be reloaded
without searching, nothing separated what a person said today from what the
store ingested a year ago, and two statements that disagree about a number
were both handed to the reader as if the store believed both.
Schema v10. Atoms gain session_id, importance, recall_count, last_recalled_at
and archived_at; two tables arrive beside them — consolidations (one watermark
per holder+scenario lane) and conflicts (a pair, the term they disagree on,
both values, and whether anyone has settled it). v_atoms_current now also
hides archived rows, and apply() drops the view before rebuilding it because
CREATE VIEW IF NOT EXISTS would keep the old body and ignore the archive.
Working memory is a read, not a store: `working <holder> <session>` returns
what that session recorded, in order, with no search involved. Consolidation
rolls a lane's new memories into one standing summary and moves the watermark
to the newest memory actually rolled up — not to the clock of the pass, which
made a second pass count the same memories twice. The archive rule is stated
in the command that runs it (recorded before N days, never returned by a
search, importance at or under a floor) rather than hidden in a decay formula
nobody measured; --dry-run names the victims first.
A conflict is recorded when a new memory repeats a label with a different
number in the same holder's same scenario. Retrieval drops the older side
while the conflict is open and both sides answer again once it is settled:
settling is not superseding, and a word-only disagreement is left alone
because a reworded sentence would fire on every paraphrase. Numbers are keyed
to both the memory's subject and the nearest preceding label, measured: the
two thermostat sentences share only their subject, so a nearest-word-only rule
found nothing.
Search now counts the memories it handed back, which is the only use estimate
the store has, and the archive rule reads it. The dense index applies the same
currency rules as the view, so an archived memory cannot stay findable through
one leg while the other hides it.
Two reads over HTTP (working, conflicts) and two tools over MCP
(working_set, memory_conflicts); curation stays with the operator because an
agent should not decide what its own past is worth. remember and supersede
carry a session id; importance does not cross MCP, because recall_count is
observed and importance is stated.
Gates: 366 passed; ruff clean; mypy 24 errors in 9 files, the recorded 9cb46c1
baseline, net 0; pyright 0 on the touched files; go build/vet/test and gofmt
clean; mcp_write_smoke PASS.
One stride over file order, truncated to the probe count, dropped the
tail of the suite: a 300-probe sample held 128 code, 86 pdf, 36
screenshot, 26 image and 24 video queries and no conversation query at
all, so every report built from it measured five of six modalities.
The budget is now split by suite weight with a floor of one probe per
modality, and a test pins that the frozen sample covers all six.
The set's question type rides along on each query, and a second metric
asks the question recall@10 cannot: when the answer lives in several
dated memories, did the newest come back first? A knowledge-update
question scores 1.000 on recall while the ranking still leads with the
value the speaker has since replaced, and that is the failure the next
task is meant to fix.
A review of the p2-1 record found seven places where the prose had drifted
away from the reports it cites. None were product defects; all were record
keeping, and record keeping is what a memory system is judged on.
The quantization row mis-transcribed nine latencies, and the two worst made
dimension truncation look twice as fast as the full scan when the report
measures six percent. The extrapolation table scaled latency by atoms and
bytes by vectors under one heading that named neither switch. The picture
index stats existed only in prose, so the harness now reads the index shape
off the artifact and times the encoder on a sample, which reproduces the
hand-timed build within seven percent. The dense arms named a store that
holds no vectors, so the report names the store the vectors came from and
refuses to run where a dense leg would rank an empty index.
The frozen baseline was migrated in place by the product CLI while the
picture index was built, so its pinned mtime, digest and schema version are
re-pinned and the step that moved it is named. The sqlite_vec import added
two mypy errors; the fix is the missing stub declaration, not an inline
ignore, and the count is back to the recorded baseline of twenty-four.
copy_store() moved into bench/scratch.py: systems.py had reached 512 lines
and bench/filesize_check fails anything over 500. The move is behaviour
identical, so the scale reports keep the commit stamp they were generated
against — the code they describe is complete at that commit.
The product fuses a sparse page, a dense vector leg, and a picture leg that
only speaks when the words come back thin. Two of the three are off by
default, and until now no committed artifact said why.
bench/leg_check.py asks the frozen suite's 300 answerable queries as their
own holder, once per configuration, and prints recall, first-hit quality,
latency and per-modality recall in one table. The picture index and the
CLIP snapshot are 48 minutes of encoding, so they are carried in rather
than rebuilt per row; the harness refuses to run a leg whose snapshot is
missing instead of quietly scoring nothing.
The numbers that came back:
- words only, 10 atoms: recall@10 0.832, MRR 0.683, p50 64ms
- words only, the configured 24: 0.848, 0.688, 69ms
- words only, page 30: 0.855, 0.690, 67ms
- dense leg on: 0.845 / 0.858, MRR down, p50 469ms / 411ms
- picture leg available: 0.815 / 0.825, MRR 0.553 / 0.551, p50 148ms / 164ms
So both defaults stay off, and the acceptance bar (recall@10 >= 0.90) is
not met by the product path yet: 0.858 is the best measured row, the
in-repo counterpart at holder scope is 0.866. The loss concentrates in
picture golds, and the current CLIP leg pays more in dilution than it
returns there.
CHECKLIST p2-1 and p0-3.7 now quote the committed reports (DENSE.md,
QUANT.md, SCALE.md, TERMS.md) instead of scratch scripts.
ZacharyZhang-NY