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neuron/tools/retrieval-eval/results-baseline-embcorpus.json
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Neuron 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 0.387     superseded   1/3  -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>
2026-08-07 14:59:39 -05:00

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