55f9ee3cb0188a0b6995cf9a1bd219265f04eeb7
5 Commits
| Author | SHA1 | Message | Date | |
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 Tim Lingo
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55f9ee3cb0 |
measure: BM25 lexical leg vs semseed baseline - net +2 (q10,q11), NOT-SHOWN
hit@5 68.6% -> 74.3%, phrase 71.4% -> 100%, MRR@10 0.461 -> 0.502, latency p50 0.97x. Zero losses, zero run-to-run drift on both sides. 2 queries moved against a 6-query noise floor: NO MEASURABLE DIFFERENCE by the harness's own test (McNemar exact p=0.50). Unaddressable records in returned slots: 57 -> 0. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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Tim Lingo
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6f3a048f36 |
feat(engram): semantically seed the graph leg (Will's HippoRAG pass, SEED_K=8)
engram_assoc_leg previously took its seeds only from the top-3 LEXICAL hits. For a paraphrase query the lexical hits are noise by construction, so the walk never reached the neighbourhood that holds the answer. This adds the seeding pass Will documents at el_runtime.c l.6082 — "Semantic seeding (HippoRAG pattern, use similarity twice): the query is embedded, the top-K nodes by cosine join the seed set" — using his own ENGRAM_EMBED_SEED_K (8). Similarity is now used twice, coherently: cosine picks where to STAND in the graph, the structural-relation walk decides what is REACHABLE, and cosine orders what was reached (iteration 2's finding, unchanged). The seed list is deliberately NOT floored at ENGRAM_EMBED_SEED_MIN. Measured over all 38 gold queries: true paraphrase targets score cosine 0.46-0.66 and the three nonsense controls' own nearest neighbours score 0.55/0.60/0.62 — the distributions OVERLAP, so no absolute cosine floor separates signal from gibberish. The gate that works is reachability: gibberish's nearest neighbours carry no structural edge, so its graph leg is empty and the controls hold. The raw top-K is selected inside the existing scoring pass, so the cosine is computed exactly once per node: no extra corpus pass, no extra embed round-trip, latency flat (p50 1220 -> 1227 ms, 1.01x). Measured vs the certified baseline feat/hybrid-semantic-recall, embedded corpus, 2 runs each, zero run-to-run drift on both sides: hit@5 51.4% -> 68.6% MRR@10 0.387 -> 0.461 paraphrase 38.5% -> 61.5% associative 0% -> 66.7% exact_rare 100% held, nonsense 2/3 held, superseded 2/3 held phrase 85.7% -> 71.4% (q11, the known rank-5 rotation tax) net +6 queries (7 fixed / 1 broken), McNemar p=0.0703 |
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Tim Lingo
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059ce02003 |
feat(engram): an associative leg on the recall path (claim 10 typed relations)
The recall route had no way to reach a node that shares no token and no embedding neighbourhood with the query. The design reserves that case for the graph, and nothing on the read path consulted an edge. This adds a third ranked leg beside the lexical and semantic ones: expand the top 3 lexical hits along STRUCTURAL relations only (claim 10 — identity, contains, superseded_by, references, ...), two hops, both directions, pruned at the same 0.02 firing threshold engram_activate uses; order what was reached by query similarity. Merged by strict rotation, never by score blending. Not PR #135. That wired recall wholesale to engram_activate and lost 57 points of phrase accuracy. The failure there was RANK, not reach — a 2-hop associate at strength 0.06 cannot outrank thousands of 1-hop neighbours of strong lexical seeds. Here the lexical leg is untouched and the associative list is empty for most queries, because a node whose only edges are `tagged` and `related` expands to nothing. MEASURED, hybrid-semantic baseline -> this, 38-query gold set, embedded corpus: associative 0.0% -> 66.7% (first non-zero ever recorded on that category) hit@5 51.4% -> 62.9% exact_rare, phrase, paraphrase, nonsense, superseded: all unchanged latency p50 1.01x 4 queries moved, all gains, 0 losses, McNemar p=0.125 deterministic: two runs of the same binary differ on 0 of 38 rows VERDICT: NOT-SHOWN. The harness needs 6 queries to clear p<0.05 and the whole associative category is only 6 queries, so even 4/6 fixed cannot reach the floor. The mechanism is confirmed to work; the gold set cannot certify it. |
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Neuron
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635453b936 |
feat(engram): rank-interleave the semantic leg into recall; embed the corpus
Replaces the score-fusion first cut with rank fusion, which is what the data called for. nomic's cosine scale is compressed (true matches 0.55-0.70, unrelated pairs 0.35-0.50), so an additive blend of cosine onto token-coverage is dominated by whichever leg has the wider spread. Alternation is invariant to both scales: L1, S1, L2, S2, ... deduped, capped at limit Lexical ranking is left byte-identical; the semantic ranking is computed beside it and admitted only above ENGRAM_EMBED_SEED_MIN (0.60) — Will's existing seed floor, no new tuning constant. That floor is what keeps the nonsense controls clean: a query with no real match must not be answered with its neighbours. embed-corpus.py / merge-corpus.py produce the derived corpus the semantic leg needs (76,986 vectors, nomic-embed-text, 0 failures, 11 min). Zero of 78,791 nodes carried an embedding before this; the field round-tripped through the snapshot but nothing ever wrote it. MEASURED, 38-query gold set, paired against the SAME derived corpus so the comparison isolates the code change: hit@5 34.3% -> 51.4% paraphrase 0.0% -> 38.5% MRR@10 0.294 -> 0.387 superseded 1/3 -> 2/3 outranks recall@10 33.3% -> 50.5% latency p50 1146 -> 1220ms (1.06x) exact_rare 100% -> 100% phrase 85.7% -> 85.7% nonsense 2/3 -> 2/3 6 queries fixed, 0 broken, McNemar exact p=0.0312, 0 drift across repeats. Regression guards all held. Contrast PR #135, which swapped the read path to spreading activation wholesale: phrase 85.7 -> 28.6, latency 2.81x. Correct mechanism, wrong substrate. The substrate is now present. Restores engram claim 24 (previously 0% honoured). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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Neuron
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cf41d12d22 |
test(retrieval): a measurement harness for memory recall, and its first verdict
Nothing else on the memory roadmap should be built until a change can be shown
to help. Right now we judge by feel, and the benchmark literature is full of
systems that felt better and measured worse. This is the missing gate.
WHAT IT MEASURES, AND WHY IT BOOTS A REAL SOUL
The subject is Will's designed retrieval — spreading activation over the
weighted directed graph, four-factor multiplicative scoring — not a proxy for
it. A Python re-implementation would measure my reading of the design, so the
harness compiles the actual soul.el amalgam from a git ref and asks it over
HTTP on /api/neuron/recall, exactly as the MCP wrapper and the app do.
BUILT ON WHAT WAS ALREADY HERE, NOT AROUND IT
docs/research/graphrag_eval/{collect,score}.py — per-query relevant-id
scoring and fixed-denominator precision@5 (kept verbatim: an empty result
should be punished like a page of junk).
docs/research-archive/p0-prototypes/eval_pinned_40q_20260715.py — the pinned
ground truth + --check winnability gate, so every run judges alike.
scripts/verify-soul-contract.sh — the isolation recipe, including the
non-obvious SOUL_ISE_URL pin without which an "isolated" soul silently
syncs the operator's live brain.
gen-soul-amalgam.sh + .gitea/workflows/ci.yaml — the build recipe and flags.
New here: ids rather than regexes as ground truth, an associative category
derived from real edges, a superseded category scored on ranking, a
machine-checked zero-lexical-overlap guarantee on paraphrases, paired
significance testing, and measurement of the real compiled soul rather than an
offline replica of one leg of it.
THE GOLD SET IS AUDITABLE, NOT VIBES
38 queries over the real 78,768-node corpus, each carrying a `derivation`
string, each re-validated by `build_gold_set.py --check`. exact_rare is mined
(document frequency 1). phrase is mined (verbatim scan; >25 matches rejected as
too diffuse). paraphrase is hand-selected then PROVEN to share zero content
words with its target — a leak fails the build, so the category cannot decay
into lexical matching. associative is derived from real hub edges with
lexically-reachable siblings dropped. nonsense is verified absent. superseded
pairs are kept only when both sides survive as distinct nodes.
HONEST ABOUT NOISE
Minimum detectable swing on 38 queries is 6: if every changed query moves the
same way, p = 2*0.5^n first clears 0.05 at n=6. Run-to-run drift is measured,
not assumed — activation is a stateful read, and it shows: main is fully
deterministic across 3 runs, the candidate drifts by 1 query. compare.py
reports "no measurable difference" for anything inside max(6, drift+1).
FIRST VERDICT — feat/recall-through-activation
hit@5 34.3% -> 22.9%, phrase 85.7% -> 28.6%, latency p50 2.81x. Five discordant
pairs, all five against the candidate, none for it; McNemar exact p = 0.0625,
so by the stated rule this is one query short of significant and is reported as
such rather than as a win for main. The latency regression is deterministic and
not in any noise band.
The benefit the branch was written for is absent: associative recall is 0/6 on
BOTH builds. Probed directly, the traversal returns the lexical seed at rank 8
and none of its 12 hub siblings. Two measured corpus facts explain it — only
4,060 of 78,768 nodes (5.2%) carry any edge, and no node has an embedding, so
the fourth factor of the four-factor product has nothing to compute from. The
mechanism runs; the corpus lacks the structure it needs.
SAFETY
Throwaway port, throwaway HOME, disposable per-run copy of the corpus; live
ports refused by name. Every soul started is killed AND confirmed dead by pid
probe, with the confirmation written into the results file; run_comparison.sh
sweeps for strays and exits non-zero if any survive. Nothing under ~/.neuron,
/Applications/Neuron*, or ~/neuron-dev-stack is read, written, or restarted.
Rung: E2E-VERIFIED — 6 full runs (3 per config) against the real compiled
binaries on the real corpus; numbers above are measured, not projected.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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