neuron/tools/retrieval-eval

Retrieval eval harness

Measures Neuron's memory retrieval so a change can be shown to help before it is believed to help. Nothing else on the memory roadmap should ship without a run through this.

tools/retrieval-eval/run_comparison.sh --baseline main --candidate <branch>

That builds a soul from each ref, boots each in isolation on a fixed corpus, runs the gold set three times per ref, and prints a table plus a verdict that refuses to call a difference real if it is inside the noise band.

What was reused

This is not a new idea, it is the missing third of an existing one.

Prior work	What it gave	What was missing
docs/research/graphrag_eval/ (collect.py, score.py, 2026-06-08)	The three-retriever comparison that produced the numbers everyone quotes: substring 1.7% P@5, graph 21.7%, BM25 55%. Per-query relevant-id scoring, fixed-denominator precision@5, unique-relevant analysis.	13 hand-written queries, judged by an LLM after the fact; measured the live soul on the live engram.
docs/research-archive/p0-prototypes/eval_pinned_40q_20260715.py	The pinned-query discipline: ground truth committed as regexes so every run judges alike, plus a --check winnability gate. 40 queries in 5 bands including a deliberate paraphrase-hard band.	Scored offline replicas of substring/BM25 — it never ran the real retrieval path.
docs/research-archive/p0-prototypes/stage0_eval_20260714.py	The hit@5 metric and the substring/BM25 reference implementations.	Same: offline only.
scripts/verify-soul-contract.sh	The isolation recipe, verbatim: throwaway port, throwaway HOME, SOUL_ENGRAM_PATH, and the non-obvious SOUL_ISE_URL pin that stops an "isolated" soul silently syncing the operator's live brain.	It is a contract gate, not a measurement.
_engine-liveness-91/gen-soul-amalgam.sh + .gitea/workflows/ci.yaml	The build recipe (elc --target=c with every .elh on the import chain removed) and CI's exact compile flags.	—

Reused directly: the isolation recipe, the build recipe, fixed-denominator precision@5, the pinned-ground-truth and winnability ideas. New here: ids rather than regexes as ground truth, an associative category derived from real graph edges, a superseded/contradicted category scored on ranking, a machine-checked zero-lexical-overlap guarantee on paraphrases, paired significance testing, and — the point — measurement against the real compiled soul rather than an offline replica of one leg of it.

Design fit

The thing under measurement is Will's designed retrieval: spreading activation over the weighted directed graph, four-factor multiplicative scoring (parent strength x edge weight x target salience x query/target cosine). A Python re-implementation would measure my reading of the design. So the harness compiles the actual soul.el amalgam and asks it over HTTP on /api/neuron/recall, exactly as the MCP wrapper and the app do.

Files

File	Does
build_gold_set.py	Derives and validates the gold set from the corpus. --check re-validates and exits non-zero if a query became unwinnable or a paraphrase leaked a word.
gold_set.json	38 queries. Every one carries a derivation string.
run_eval.py	Boots one soul in isolation, runs the gold set, writes metrics. Kills and confirms dead its child; records the confirmation in the results file.
compare.py	Paired diff of two result files with McNemar's exact test and a stated noise floor.
build-soul.sh	Compiles a soul binary from a plain source tree.
run_comparison.sh	All of the above, end to end, from two git refs.

The gold set — 38 queries

Built from the real corpus (snapshot-pre-repair-20260806.json, 78,768 nodes / 14,214 edges) so it reflects one person's accumulating memory, not document QA.

Category	n	Expected answer derived by
exact_rare	6	Mined. Tokens with document frequency 1 across all 78,768 nodes, whose single containing node is a 300–6000 char Memory/Knowledge/Belief. That node is the only possible answer. Re-verified every build.
phrase	7	Mined. Case-insensitive verbatim scan; the matching set is the answer key. Phrases matching >25 nodes are rejected as too diffuse.
paraphrase	13	Hand-selected, machine-checked. Target locked by id; the build then proves that zero content words of the query appear anywhere in the target's label, content, or tags. A leak fails the build — the category cannot quietly decay into lexical matching.
associative	6	Derived from edges. Query built from one value node's distinctive vocabulary; expected answers are its siblings on the Self - Values (grounded) hub. Siblings sharing any query word are dropped, so the only route from query to answer is seed -> hub -> sibling.
nonsense	3	Control. Verified that no token occurs anywhere in the corpus. Correct behaviour is to return nothing.
superseded	3	Derived. Correction/stale pairs located by regex scan, kept only when both sides resolve to different surviving nodes. Scored on ranking: the correction must be returned and rank above the stale node.

Metrics

hit@5, recall@5, recall@10, precision@5 (fixed denominator 5, so an empty result is punished like a page of junk), MRR@10, and wall-clock latency per query (p50/p95/max). Output is a table plus a machine-readable JSON per run so runs can be diffed.

Honesty about noise

• Minimum detectable swing on this 38-query set: 6 queries. If every query that changes changes the same way, p = 2 x 0.5^n, which first drops under 0.05 at n=6. Any net change smaller than that is inside the noise band and compare.py says so in those words.
• Run-to-run drift is measured, not assumed. Activation is a stateful read by design (traversal reinforces what it touches), so identical inputs need not give identical outputs. Observed: main 0 queries of drift across 3 runs (fully deterministic); the activation branch 1 query.
• The noise floor used for the verdict is max(6, observed_drift + 1).
• This gold set is underpowered for small effects. A genuine 3-query improvement would not clear the bar. Growing the set is the fix; until then, a small positive delta means "not shown", not "no effect".

First result: main vs feat/recall-through-activation

Corpus and gold set identical, three runs each, fresh corpus copy per run.

	main	recall-through-activation	delta
hit@5	34.3%	22.9%	-11.4pp
recall@5	26.9%	19.1%	-7.9pp
recall@10	33.3%	24.3%	-9.1pp
precision@5	12.0%	7.4%	-4.6pp
MRR@10	0.294	0.242	-0.053
latency p50	1140 ms	3209 ms	2.81x
latency p95	1584 ms	4852 ms	3.06x
nonsense clean	2/3	2/3	—
superseded outranks	1/3	0/3	-1

By category (hit@5):

category	main	activation
exact_rare	100%	100%
phrase	85.7%	28.6%
paraphrase	0%	0%
associative	0%	0%
superseded	0%	0%

Verdict: directionally worse, one query short of significant. 5 discordant pairs, all 5 against the candidate, 0 for it. McNemar exact p = 0.0625 — under the stated rule that is inside the noise band, so the harness reports "no measurable difference" on accuracy and the honest summary is "5 for 5 the wrong way, needs a 6th or a larger gold set to call".

Latency is a different story: 2.8x at p50 is deterministic and far outside any noise band. That regression is real.

The result the branch was written for did not appear. Its stated purpose was to recover sibling nodes one hub-hop away — the associative category — and that category is 0/6 on both builds. Probing directly: for the query Marines hernia sepsis medical ward, the activation build returns the lexical seed node itself at rank 8, and none of its 12 hub siblings anywhere in the top 10. The traversal is running; it is not reaching siblings.

Two corpus facts likely explain it, and both are measurable rather than speculative:

1. The graph is nearly edgeless. Only 4,060 of 78,768 nodes (5.2%) carry any edge at all — 14,214 edges total, 0.18 per node. Spreading activation over a graph with no edges is an expensive way to do lexical matching, which is roughly what the numbers show.
2. No embeddings. No node in this snapshot has an embedding field, so the fourth factor of the four-factor product — query/target cosine similarity — has nothing to compute from, and the semantic seeding pass is inert.

That is the harness earning its keep on its first job: the change would have felt like progress (it is the designed mechanism, and it does run) and measures as a regression on phrase queries plus a 2.8x latency cost, with its intended benefit unrealised because the corpus lacks the structure it needs.