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exact_rare 100% held, nonsense 2/3 held, superseded 2/3 held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
Tim Lingo