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test(retrieval): a measurement harness for memory recall, and its first verdict

Browse Source 
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derived from real edges, a superseded category scored on ranking, a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 superseded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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tools/retrieval-eval/README.md
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# 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.
tools/retrieval-eval/build-soul.sh
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#!/usr/bin/env bash
# build-soul.sh — compile a soul binary from a plain source tree (no git needed).
#
# Reuses the amalgam recipe worked out in gen-soul-amalgam.sh (round 9.1) and the
# compile flags from .gitea/workflows/ci.yaml, so the binary under test is the
# same translation unit CI ships — not a re-implementation.
#
# elc --target=c emits only an extern prototype for any module that has a .elh
# header beside it, and inlines the module's bodies when it does not. So the
# amalgam is produced in a scratch copy with every .elh on the import chain
# deleted.
#
# usage: build-soul.sh <src-tree-with-*.el> <out-binary>
set -euo pipefail
SRC="${1:?usage: build-soul.sh <src-tree> <out-binary>}"
OUT="${2:?out-binary}"
ELC="${ELC:-$HOME/neuron-dev-stack/src/el/lang/dist/platform/elc}"
EL_REPO="${EL_REPO:-$HOME/Development/neuron-technologies/el}"
RTDIR="${RTDIR:-$SRC/vendor/el-runtime/v1.0.0-20260501}"
SSL="${SSL_PREFIX:-/opt/homebrew/opt/openssl@3}"
[ -x "$ELC" ] || { echo "no elc at $ELC" >&2; exit 2; }
[ -f "$RTDIR/el_runtime.c" ] || { echo "no el_runtime.c at $RTDIR" >&2; exit 2; }
GEN="$(mktemp -d "${TMPDIR:-/tmp}/soul-build.XXXXXX")"
trap 'rm -rf "$GEN"' EXIT
mkdir -p "$GEN/neuron" "$GEN/foundation/el/elp/src"
cp "$SRC"/*.el "$GEN/neuron/"
cp "$EL_REPO"/elp/src/*.el "$GEN/foundation/el/elp/src/"
find "$GEN" -name '*.elh' -delete
( cd "$GEN/neuron" && "$ELC" --target=c soul.el ) > "$GEN/soul.c"
BODIES=$(grep -c '^el_val_t .*) {$' "$GEN/soul.c" || true)
echo "[build-soul] amalgam $(wc -c < "$GEN/soul.c" | tr -d ' ') bytes, ${BODIES} inlined bodies"
[ "$BODIES" -ge 1200 ] || { echo "[build-soul] FAIL: only $BODIES bodies — an import was not inlined"; exit 1; }
cc -O2 -DHAVE_CURL -rdynamic \
-I"$RTDIR" -I"$SSL/include" -L"$SSL/lib" \
"$GEN/soul.c" "$RTDIR/el_runtime.c" \
-lssl -lcrypto -lcurl -lpthread -lm \
-o "$OUT" 2> "$GEN/cc.log" || { echo "[build-soul] FAIL compile"; tail -40 "$GEN/cc.log"; exit 1; }
if grep -qE 'implicit.*(engram_|el_)' "$GEN/cc.log"; then
echo "[build-soul] FAIL: implicit declarations of runtime symbols"; grep -E 'implicit' "$GEN/cc.log" | head; exit 1; fi
echo "[build-soul] OK -> $OUT ($(wc -c < "$OUT" | tr -d ' ') bytes)"
tools/retrieval-eval/build_gold_set.py
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#!/usr/bin/env python3
"""
build_gold_set.py — derive the retrieval gold set FROM the corpus, and validate it.
WHY THIS FILE EXISTS AS CODE AND NOT AS A HAND-WRITTEN JSON
A gold set nobody can audit is vibes with extra steps. Every expected answer
here is either (a) mined from the corpus by a rule this script re-runs, or
(b) hand-selected with a stated criterion that this script then CHECKS
against the corpus. Both leave a `derivation` string on every query, and the
checks are re-run on demand so the set cannot silently rot as the corpus
changes.
Lineage: this extends the pinned-query approach from
docs/research-archive/p0-prototypes/eval_pinned_40q_20260715.py (pinned
ground-truth patterns + a --check "winnability" gate) and the per-query
relevant-id scoring from docs/research/graphrag_eval/score.py. What is new:
ids as ground truth rather than regexes alone, an ASSOCIATIVE category
derived from real graph edges, a superseded/contradicted category, and a
machine-checked no-lexical-overlap guarantee on the paraphrase category.
THE SIX CATEGORIES, AND WHAT EACH ONE IS FOR
exact_rare a single rare word. Substring matching already wins these.
They are a REGRESSION GUARD: any change that loses them is
disqualified regardless of what else it gains.
phrase a multi-word string that exists verbatim in the corpus.
Guards multi-token queries, which the old substring matcher
handled by returning nothing.
paraphrase same meaning, ZERO shared content words with the target node.
THE CATEGORY THAT MATTERS. Mechanically unreachable by string
matching; reachable only by semantics or by association.
associative the answer is one hub-hop from an obvious starting point and
shares no words with the query. This is the case the graph is
supposed to buy: query one value, get its siblings.
nonsense must return nothing. Guards against a retriever that "improves"
recall by returning the whole graph.
superseded a fact that was later corrected. The correction must OUTRANK
the stale version — ranking, not mere presence.
usage:
python3 build_gold_set.py <snapshot.json> [--out gold_set.json] [--check]
--check re-validates an existing gold_set.json against the corpus and exits
non-zero if any query became unwinnable or any paraphrase leaked a word.
"""
import argparse
import json
import os
import re
import sys
from collections import Counter, defaultdict
HERE = os.path.dirname(os.path.abspath(__file__))
DEFAULT_OUT = os.path.join(HERE, "gold_set.json")
TOKEN = re.compile(r"[a-z0-9][a-z0-9\-']*")
# Stopwords are deliberately generous. A paraphrase query is only interesting if
# its CONTENT words are absent from the target; "the", "is", "what" appearing in
# both proves nothing. Being generous here makes the overlap test STRICTER on
# the words that carry meaning, which is the conservative direction.
STOP = set("""
a about above after again against all also am an and any are aren't as at be because been
before being below between both but by can can't cannot could couldn't did didn't do does
doesn't doing don't down during each few for from further had hadn't has hasn't have haven't
having he her here hers herself him himself his how i if in into is isn't it its itself just
me more most my myself no nor not of off on once only or other others ought our ours ourselves
out over own same shan't she should shouldn't so some such than that the their theirs them
themselves then there these they this those through to too under until up very was wasn't we
were weren't what when where which while who whom why will with won't would wouldn't you your
yours yourself yourselves get gets got make makes made take takes use uses used way ways thing
things does doing done keep keeps kept go goes going come comes came one two something anything
""".split())
# ─────────────────────────────────────────────────────────────────────────────
# corpus helpers
# ─────────────────────────────────────────────────────────────────────────────
def load_corpus(path):
with open(path, encoding="utf-8", errors="replace") as fh:
data = json.load(fh)
nodes = [n for n in data.get("nodes", []) if isinstance(n, dict) and n.get("id")]
edges = [e for e in data.get("edges", []) if isinstance(e, dict)]
return nodes, edges
def doctext(n):
return " ".join([str(n.get("label") or ""), str(n.get("content") or ""), str(n.get("tags") or "")])
def content_tokens(s):
return {t for t in TOKEN.findall(s.lower()) if t not in STOP and len(t) > 2}
# ─────────────────────────────────────────────────────────────────────────────
# hand-authored queries. Every entry states HOW its expected answer was chosen.
# The `check` field names the validation this script runs against the corpus.
# ─────────────────────────────────────────────────────────────────────────────
# EXACT_RARE — mined, not chosen. The rule (re-run by mine_exact_rare below):
# tokens whose document frequency across the whole corpus is 1, whose single
# containing node is a Memory/Knowledge/Belief with 300-6000 chars of content
# (so the answer is a real memory, not a 117KB whitepaper that contains every
# word in English), and whose token is plain lowercase alphabetic. The expected
# answer is that one node — it is the only node that can possibly be correct.
EXACT_RARE_SEEDS = [
"unjailbreakable",
"engram-migrate",
"cartabandonedevent",
"pre-apprenticeship",
"inferencenodemanager",
"clear-eyed",
]
# PHRASE — chosen by reading the corpus for phrases that (a) occur verbatim,
# (b) occur in a small enough set of nodes that "relevant" is well defined.
# Expected answers are computed here as EVERY node whose text contains the
# phrase case-insensitively — so the answer set is a fact about the corpus, not
# an opinion. Queries whose phrase matches more than PHRASE_MAX nodes are
# rejected by validation as too diffuse to score.
PHRASE_MAX = 25
PHRASE_SEEDS = [
("patterns not returns",
"a verbatim correction Will issued; expected = every node containing the phrase"),
("thirty moves",
"the canonical biographical phrase; expected = every node containing it"),
("Grandma Lucas",
"a named person appearing verbatim in the biography/value nodes"),
("Directed Harmonic",
"the canonical DHARMA expansion, confirmed by Will April 24 2026"),
("Sarah Bishop",
"a named person; rare enough that the answer set is unambiguous"),
("Directed Autonomous Runtime Modification",
"the DARMA expansion, quoted verbatim in the backlog item and its correction"),
("zero-knowledge encrypted backup",
"the paid-tier feature name as written in the roadmap nodes"),
]
# PARAPHRASE — hand-authored. THE SELECTION CRITERION, stated once and applied
# to all nine: pick a node whose SUBJECT is unmistakable to a reader, then write
# the query a person would actually type when they remember the subject but not
# the words. The target is then LOCKED by id, and this script enforces the hard
# property that makes the category meaningful: not one content word of the query
# appears anywhere in the target node's label, content, or tags. If a word
# leaks, validation fails and the query must be rewritten — the set cannot
# quietly degrade into a lexical query wearing a paraphrase costume.
PARAPHRASE_SEEDS = [
("kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"the elderly relative who passed while he stayed away",
"target: 'Value - Do the Essential Thing While You Can', whose subject is Grandma Lucas "
"dying in Feb 2006 without Will saying goodbye. Query names the event with none of the "
"node's own vocabulary."),
("kn-58874a74-b96f-4883-9e08-45707f4bd3ee",
"a soldier sidelined by illness who refused to quit",
"target: 'Value - Survival Is Not an Excuse to Stop', whose subject is enlisting in the "
"Marines, a severe hernia, and sepsis. Query describes the episode obliquely."),
("kn-13f60407-7b70-4db1-964f-ea1f8196efbd",
"choosing an uncomfortable fact over a pleasant fiction",
"target: 'Value - Honesty Before Comfort'. Query states the principle in wholly "
"different words."),
("kn-22d77abe-b3c5-42fd-afcd-dcb87d924929",
"a tight payload beats a bloated one",
"target: 'Value - Precision Over Brute Force'. Query restates the claim with no "
"shared vocabulary."),
("kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
"if you are able and nobody is coming the job is yours",
"target: 'Value - Capability Is a Debt You Owe the Moment'. Query states the "
"obligation without the node's terms."),
("kn-0bb4f021-56de-4947-a35b-a37209e7ba21",
"learning is the wealth creditors cannot seize",
"target: 'Value - Knowledge Survives When Nothing Else Does', whose subject is the "
"library following Will across 30+ moves."),
("kn-5de5a9ac-fd15-45ab-bf18-77566781cf40",
"reliability proven by track record not assertion",
"target: 'Value - Earned Trust' ('Trust is demonstrated, not declared')."),
("kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83",
"boundaries that enable instead of confine",
"target: 'Value - Constraints as Freedom'. Query is a restatement of the same claim."),
("kn-78db5396-3dbc-4481-bfc7-e4e1422feb1c",
"what shifts tells you where to cut a system apart",
"target: 'Value - Change Is the Signal', the value VBD is built on."),
("kn-f230b362-b201-4402-9833-4160c89ab3d4",
"a mind that compounds instead of resetting each day",
"target: 'Value - The System Must Accumulate'. Query is the accumulation claim in "
"different vocabulary."),
("kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"loved for the unedited self and not the polished exterior",
"target: 'Value - Being Seen Is Rarer Than Being Known', whose subject is Sarah Bishop "
"as the first person Will did not perform for."),
("kn-e0423482-cfa5-4796-8689-8495c93b66bc",
"cheerfulness you arrive at instead of assuming",
"target: 'Value - Hope Is a Conclusion'. Query restates 'a conclusion, not a premise'."),
("kn-6061318f-046b-4935-907d-8eafdce14930",
"a childhood offering no solid foundation to inherit",
"target: 'Value - Structure Is Not Inherited', whose subject is thirty moves between "
"two parents' collapses."),
]
# ASSOCIATIVE — derived from real edges, not authored. The construction:
# every value node hangs off the 'Self - Values (grounded)' hub by an `identity`
# edge. For a chosen value node V, the query is built from V's own distinctive
# vocabulary; the expected answers are V's SIBLINGS on that hub. A sibling
# shares no query words with the query by construction (validated below), so the
# only path from the query to a sibling is: lexical seed on V -> hub -> sibling.
# That is a two-hop traversal and nothing else can produce it.
VALUES_HUB = "kn-5b606390-a52d-4ca2-8e0e-eba141d13440"
ASSOCIATIVE_SEEDS = [
("kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71", "Grandma Lucas stroke February 2006 goodbye window"),
("kn-58874a74-b96f-4883-9e08-45707f4bd3ee", "Marines hernia sepsis medical ward"),
("kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e", "Sarah Bishop Dyer trailer performance"),
("kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83", "Swarm Architecture containment lateral worker"),
("kn-e0423482-cfa5-4796-8689-8495c93b66bc", "hope won inside the narrative preface"),
("kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8", "man of the house six years old expectation"),
]
# NONSENSE — must return nothing. Strings chosen to be lexically impossible:
# validation asserts each appears in ZERO corpus nodes as a substring and that
# none of its tokens appears anywhere either (so not even a partial seed exists).
NONSENSE_SEEDS = [
"zqxjvw plimforth grebulon",
"flarnbistle quommetry",
"xxqzzt vurblenacht throom",
]
# SUPERSEDED — a fact that was corrected. Chosen by searching the corpus for
# explicit correction language and keeping pairs where BOTH the stale statement
# and its correction exist as separate nodes. Scored on RANKING: the correction
# must appear, and must appear above the stale node. Ids are locked here and
# validated to exist and to match their stated role.
SUPERSEDED_SEEDS = [
# (query, correct_id, stale_id, derivation)
]
# ─────────────────────────────────────────────────────────────────────────────
# mining
# ─────────────────────────────────────────────────────────────────────────────
def mine_exact_rare(nodes, byid, seeds):
"""Re-derive: confirm each seed token still has df==1 and name its node."""
tok = re.compile(r"[A-Za-z][A-Za-z0-9\-]{4,}")
want = set(seeds)
df = Counter()
post = defaultdict(set)
for n in nodes:
for t in {w.lower() for w in tok.findall(doctext(n))}:
if t in want:
df[t] += 1
post[t].add(n["id"])
out = []
for s in seeds:
ids = sorted(post.get(s, ()))
out.append((s, ids, df.get(s, 0)))
return out
def phrase_matches(nodes, phrase):
p = phrase.lower()
return sorted(n["id"] for n in nodes if p in doctext(n).lower())
def hub_siblings(edges, hub, relation="identity"):
sibs = []
for e in edges:
if e.get("from_id") == hub and e.get("relation") == relation:
sibs.append(e["to_id"])
elif e.get("to_id") == hub and e.get("relation") == relation:
sibs.append(e["from_id"])
return list(dict.fromkeys(sibs))
def find_superseded_pairs(nodes, byid):
"""Locked pairs, each verified here to exist and to carry its stated marker.
Chosen by scanning the corpus for explicit correction language
(CORRECTION/SUPERSEDES/re-corrected/no longer/RECONCILED) and keeping only
cases where the STALE claim also survives as its own node — a supersession
with nothing to outrank is not a ranking test.
"""
pairs = []
txt = {n["id"]: doctext(n) for n in nodes}
def find_one(pattern, exclude=()):
rx = re.compile(pattern)
return [n["id"] for n in nodes
if n["id"] not in exclude
and rx.search(txt[n["id"]])
and 150 < len(str(n.get("content") or "")) < 12000
and n.get("node_type") in ("Memory", "Knowledge", "Belief", "BacklogItem")]
# Each entry: (query, correction-pattern, stale-pattern, why).
# The stale side is searched with the correction hits EXCLUDED, because most
# correction memories quote the claim they are killing — without the
# exclusion the "stale" node resolves to the correction itself and the pair
# collapses into a no-op. A pair is only emitted if both sides resolve to
# DIFFERENT surviving nodes; otherwise it is dropped and reported.
SPECS = [
("is the self-improvement architecture called DARMA or DHARMA",
r'(?i)CORRECTION:.{0,90}DHARMA .{0,12}not DARMA',
r'(?i)\bDARMA\b',
"correction node is Will's confirmation that the H is intentional (DHARMA, not DARMA); "
"the stale node is the surviving backlog item still titled 'Implement DARMA'."),
("how many provisional patents does Will actually have",
r'(?i)EXACTLY 6 (fully-specced )?provisional',
r'(?i)(MY ARCHITECTURE = 12 filed patents|\b12 filed patents\b)',
"correction node is the 2026-06-17 confabulation flag establishing EXACTLY 6 provisionals; "
"the stale node is the surviving memory that asserts 12 filed patents."),
("is MCP still the live integration layer",
r'(?i)MCP RETIRED',
r'(?i)MCP server live at',
"correction node is the 'CGI ARCHITECTURE - THREE LAYERS, MCP RETIRED' decision of "
"April 30 2026; the stale node still records the MCP server as live."),
("what does the patterns-not-returns directive mean",
r'(?i)CORRECTION:.{0,80}patterns not returns',
r'(?i)established returns',
"correction node is Will's 'patterns not returns' correction; the stale node is a "
"surviving node carrying the misread 'established returns' directive."),
("was the earlier identity-bug finding correct",
r'(?i)SUPERSEDES the earlier .critical identity bug',
r'(?i)critical identity bug',
"correction node explicitly supersedes the 'critical identity bug' finding; the stale "
"node is the surviving original finding."),
("does Neuron have recursive self-improvement",
r'(?i)twice answered .Neuron has no recursive self-improvement',
r'(?i)no recursive self-improvement',
"correction node records the June-29 finding that the CGI provisional IS the "
"recursive-self-improvement mechanism; the stale node is the surviving denial."),
]
for query, cpat, spat, why in SPECS:
corr = find_one(cpat)
if not corr:
continue
stale = find_one(spat, exclude=set(corr))
if not stale:
continue
pairs.append((query, corr[0], stale[0], why))
return pairs
# ─────────────────────────────────────────────────────────────────────────────
# build
# ─────────────────────────────────────────────────────────────────────────────
def build(nodes, edges):
byid = {n["id"]: n for n in nodes}
tokset = {n["id"]: content_tokens(doctext(n)) for n in nodes}
queries = []
problems = []
qn = [0]
def add(cat, query, relevant, derivation, **extra):
qn[0] += 1
q = {
"id": f"q{qn[0]:02d}",
"category": cat,
"query": query,
"relevant": sorted(relevant),
"derivation": derivation,
}
q.update(extra)
queries.append(q)
return q
# --- exact_rare ---------------------------------------------------------
for tokname, ids, df in mine_exact_rare(nodes, byid, EXACT_RARE_SEEDS):
if df != 1 or len(ids) != 1:
problems.append(f"exact_rare '{tokname}': df={df}, ids={len(ids)} (expected df=1)")
continue
lab = (byid[ids[0]].get("label") or "")[:60]
add("exact_rare", tokname, ids,
f"MINED: token '{tokname}' has document frequency 1 over all {len(nodes)} corpus nodes "
f"(re-verified at build time). Its single containing node is {ids[0]} "
f"('{lab}'), which is therefore the only possible correct answer.")
# --- phrase -------------------------------------------------------------
for phrase, why in PHRASE_SEEDS:
ids = phrase_matches(nodes, phrase)
if not ids:
problems.append(f"phrase '{phrase}': 0 corpus matches — unwinnable")
continue
if len(ids) > PHRASE_MAX:
problems.append(f"phrase '{phrase}': {len(ids)} matches > {PHRASE_MAX} — too diffuse")
continue
add("phrase", phrase, ids,
f"MINED: {why}. Case-insensitive verbatim substring scan over label+content+tags at "
f"build time returns exactly {len(ids)} node(s); that set IS the answer key.")
# --- paraphrase ---------------------------------------------------------
for target, query, why in PARAPHRASE_SEEDS:
if target not in byid:
problems.append(f"paraphrase target {target} not in corpus")
continue
qt = content_tokens(query)
leak = sorted(qt & tokset[target])
if leak:
problems.append(f"paraphrase '{query}': leaks {leak} into target {target}")
continue
add("paraphrase", query, [target],
f"HAND-SELECTED with criterion: {why} VERIFIED at build time: of the {len(qt)} content "
f"words in the query, ZERO appear anywhere in the target's label, content, or tags — so "
f"no string-matching retriever can reach this answer.",
zero_overlap_verified=True, query_content_words=sorted(qt))
# --- associative --------------------------------------------------------
sibs = hub_siblings(edges, VALUES_HUB)
if len(sibs) < 5:
problems.append(f"associative: values hub {VALUES_HUB} has only {len(sibs)} siblings")
for src, query in ASSOCIATIVE_SEEDS:
if src not in byid or src not in sibs:
problems.append(f"associative source {src} not a sibling on {VALUES_HUB}")
continue
qt = content_tokens(query)
others = [s for s in sibs if s != src and s in byid]
# A sibling only counts as a legitimate expected answer if the query
# cannot reach it lexically. Drop any sibling that shares a content word.
clean = [s for s in others if not (qt & tokset[s])]
dropped = len(others) - len(clean)
if len(clean) < 5:
problems.append(f"associative '{query}': only {len(clean)} lexically-unreachable siblings")
continue
add("associative", query, clean,
f"DERIVED FROM EDGES: the query is built from the distinctive vocabulary of {src} "
f"('{(byid[src].get('label') or '')[:48]}'), which hangs off the values hub {VALUES_HUB} "
f"by an `identity` edge. Expected answers are that node's SIBLINGS on the same hub "
f"({len(clean)} of {len(others)}; {dropped} dropped because they shared a query word and "
f"so were lexically reachable). Every remaining sibling shares ZERO content words with "
f"the query — the only route from query to answer is seed({src}) -> hub -> sibling, a "
f"two-hop traversal.",
associative_source=src, hub=VALUES_HUB, siblings_dropped_for_overlap=dropped)
# --- nonsense -----------------------------------------------------------
all_tokens = set()
for n in nodes:
all_tokens |= {t for t in TOKEN.findall(doctext(n).lower())}
for s in NONSENSE_SEEDS:
present = sorted(t for t in TOKEN.findall(s.lower()) if t in all_tokens)
if present:
problems.append(f"nonsense '{s}': tokens {present} DO occur in corpus")
continue
add("nonsense", s, [],
f"CONTROL: verified at build time that none of this string's tokens occurs anywhere in "
f"the corpus. Correct behaviour is to return NOTHING; any result is a false positive.",
expect_empty=True)
# --- superseded ---------------------------------------------------------
for query, correct, stale, why in find_superseded_pairs(nodes, byid):
if correct not in byid or stale not in byid:
problems.append(f"superseded '{query}': id missing from corpus")
continue
add("superseded", query, [correct],
f"DERIVED: {why} Scored on RANKING, not presence: the corrected node {correct} must be "
f"returned AND must rank above the stale node {stale}.",
must_outrank=[correct, stale],
stale_id=stale,
correct_label=(byid[correct].get("label") or "")[:70],
stale_label=(byid[stale].get("label") or "")[:70])
return queries, problems
def summarize(queries):
c = Counter(q["category"] for q in queries)
return ", ".join(f"{k}={c[k]}" for k in
("exact_rare", "phrase", "paraphrase", "associative", "nonsense", "superseded")
if c[k])
def main():
ap = argparse.ArgumentParser()
ap.add_argument("snapshot")
ap.add_argument("--out", default=DEFAULT_OUT)
ap.add_argument("--check", action="store_true",
help="validate only; do not write. Non-zero exit if anything is unwinnable.")
args = ap.parse_args()
nodes, edges = load_corpus(args.snapshot)
print(f"corpus: {len(nodes)} nodes, {len(edges)} edges ({os.path.basename(args.snapshot)})")
queries, problems = build(nodes, edges)
print(f"gold set: {len(queries)} queries [{summarize(queries)}]")
if problems:
print(f"\n{len(problems)} PROBLEM(S) — these queries were REJECTED, not silently kept:")
for p in problems:
print(" -", p)
if args.check:
sys.exit(1 if problems else 0)
doc = {
"corpus": os.path.abspath(args.snapshot),
"corpus_nodes": len(nodes),
"corpus_edges": len(edges),
"note": ("Every query carries a `derivation` recording how its expected answer was chosen. "
"Re-run with --check to re-validate the whole set against the corpus."),
"queries": queries,
}
with open(args.out, "w", encoding="utf-8") as fh:
json.dump(doc, fh, indent=1, ensure_ascii=False)
print(f"\nwrote {args.out}")
if __name__ == "__main__":
main()
tools/retrieval-eval/compare.py
Executable
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#!/usr/bin/env python3
"""
compare.py — diff two run_eval.py result files, WITH a noise threshold.
WHY THE STATISTICS ARE NOT OPTIONAL
With ~35 scored queries, one query is ~2.9 percentage points. A harness that
reports "hit@5 improved 2.9%" without saying that is one query is a harness
that will approve noise. So this file refuses to call anything an
improvement on the strength of the headline number alone. It reports:
1. The DISCORDANT PAIRS. Two configurations scored on the same queries are
paired data, so the only queries carrying information are the ones
where they disagree: b = fixed by B, c = broken by B. Queries both got
right, or both got wrong, tell you nothing about which is better.
2. McNEMAR'S EXACT TEST on (b, c). Under the null "the change is a coin
flip", the discordant outcomes are Binomial(b+c, 0.5). The two-sided
exact p-value is computed here with no scipy dependency.
3. The MINIMUM DETECTABLE SWING for this gold set: the smallest number of
net-changed queries that would reach p < 0.05 if every discordant pair
fell the same way. Anything smaller is inside the noise band, and the
verdict line says so in those words.
Repeat-run variance is the other half of honesty. Spreading activation is a
stateful read (it reinforces what it touches), so identical inputs need not
give identical outputs. Pass --repeats to fold several runs of the same
config into an observed variance band; a delta inside that band is not real
either, however good its p-value looks.
usage:
python3 compare.py --baseline results-main.json --candidate results-act.json
python3 compare.py --baseline a.json --candidate b.json \
--repeats-baseline a2.json a3.json --repeats-candidate b2.json b3.json
"""
import argparse
import json
from math import comb
def binom_two_sided(b, c):
"""Two-sided exact binomial p for b successes in n=b+c at p=0.5."""
n = b + c
if n == 0:
return 1.0
k = min(b, c)
tail = sum(comb(n, i) for i in range(0, k + 1)) / (2 ** n)
return min(1.0, 2 * tail)
def min_detectable_swing(n_scored, alpha=0.05):
"""Smallest all-one-way discordant count reaching p < alpha.
If every query that changes changes in the same direction, the p-value is
2 * 0.5**n. Solve for the smallest n where that drops under alpha. This is
the FLOOR: any real change will have some discordance both ways, so the true
requirement is larger. Reporting the floor is the conservative move — it is
the most generous threshold we would ever accept.
"""
n = 1
while n <= n_scored:
if 2 * (0.5 ** n) < alpha:
return n
n += 1
return n_scored
def load(path):
with open(path, encoding="utf-8") as fh:
return json.load(fh)
def row_map(doc):
return {r["id"]: r for r in doc["rows"]}
def outcome(r):
"""Binary per-query outcome used for the paired test.
hit@5 for scored queries; 'returned nothing' for the nonsense controls;
'correction outranks the stale node' for the superseded queries. One number
per query, so every query votes exactly once.
"""
if "clean" in r:
return 1.0 if r["clean"] else 0.0
if "outranks" in r:
return 1.0 if r["outranks"] else 0.0
return r.get("hit@5") or 0.0
def band(values):
return (min(values), max(values))
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--baseline", required=True)
ap.add_argument("--candidate", required=True)
ap.add_argument("--repeats-baseline", nargs="*", default=[])
ap.add_argument("--repeats-candidate", nargs="*", default=[])
ap.add_argument("--out", default=None)
args = ap.parse_args()
A, B = load(args.baseline), load(args.candidate)
ra, rb = row_map(A), row_map(B)
ids = [q for q in ra if q in rb]
n = len(ids)
aa, ab = A["aggregate"], B["aggregate"]
print(f"baseline {A['label']:14} soul={A['soul_md5'][:12]} {n} shared queries")
print(f"candidate {B['label']:14} soul={B['soul_md5'][:12]}")
print(f"corpus {A['corpus_nodes']} nodes / {A['corpus_edges']} edges "
f"(identical copy for both runs)\n")
metrics = [("hit@5", 1), ("recall@5", 1), ("recall@10", 1),
("precision@5", 1), ("mrr@10", 0)]
print(f" {'metric':14} {'baseline':>10} {'candidate':>10} {'delta':>10}")
for m, as_pct in metrics:
x, y = aa[m], ab[m]
if as_pct:
print(f" {m:14} {100*x:>9.1f}% {100*y:>9.1f}% {100*(y-x):>+9.1f}pp")
else:
print(f" {m:14} {x:>10.3f} {y:>10.3f} {y-x:>+10.3f}")
for m in ("latency_ms_p50", "latency_ms_p95"):
x, y = aa[m], ab[m]
ratio = f"{y/x:.2f}x" if x else "n/a"
print(f" {m:14} {x:>9.0f}ms {y:>9.0f}ms {ratio:>10}")
print(f" {'nonsense':14} {aa['nonsense_clean']:>10} {ab['nonsense_clean']:>10}")
print(f" {'outranks':14} {aa['superseded_outranks']:>10} {ab['superseded_outranks']:>10}")
print(f"\n {'category':14} {'n':>3} {'base hit@5':>11} {'cand hit@5':>11} {'delta':>9}")
for c in sorted(set(aa["by_category"]) & set(ab["by_category"])):
ea, eb = aa["by_category"][c], ab["by_category"][c]
if c == "nonsense":
print(f" {c:14} {ea['n']:>3} {'clean ' + str(ea['clean']):>11} "
f"{'clean ' + str(eb['clean']):>11}")
else:
print(f" {c:14} {ea['n']:>3} {100*ea['hit@5']:>10.1f}% {100*eb['hit@5']:>10.1f}% "
f"{100*(eb['hit@5']-ea['hit@5']):>+8.1f}pp")
# ---- paired significance -------------------------------------------------
fixed, broken = [], []
for q in ids:
oa, ob = outcome(ra[q]), outcome(rb[q])
if ob > oa:
fixed.append(q)
elif ob < oa:
broken.append(q)
b, c = len(fixed), len(broken)
p = binom_two_sided(b, c)
mds = min_detectable_swing(n)
print(f"\n== paired comparison over {n} queries ==")
print(f" fixed by candidate : {b} {[ra[q]['category'] + ':' + q for q in fixed]}")
print(f" broken by candidate: {c} {[ra[q]['category'] + ':' + q for q in broken]}")
print(f" discordant pairs : {b + c} net {b - c:+d} queries")
print(f" McNemar exact p : {p:.4f}")
print(f" noise threshold : a difference needs at least {mds} queries moving the "
f"same way to clear p<0.05 on this {n}-query set")
# ---- repeat-run variance -------------------------------------------------
var = {}
for name, paths, first in (("baseline", args.repeats_baseline, A),
("candidate", args.repeats_candidate, B)):
docs = [first] + [load(p) for p in paths]
if len(docs) > 1:
hits = [d["aggregate"]["hit@5"] for d in docs]
lo, hi = band(hits)
spread_q = round((hi - lo) * first["aggregate"]["n_scored"])
var[name] = {"runs": len(docs), "hit@5_min": lo, "hit@5_max": hi,
"spread_queries": spread_q}
print(f" {name} repeat runs ({len(docs)}): hit@5 {100*lo:.1f}%..{100*hi:.1f}% "
f"= {spread_q} query of run-to-run drift")
drift = max([v["spread_queries"] for v in var.values()], default=0)
floor = max(mds, drift + 1)
print("\n== VERDICT ==")
net = b - c
if abs(net) < floor:
print(f" NO MEASURABLE DIFFERENCE. Net {net:+d} queries is inside the noise band "
f"(needs |net| >= {floor}: {mds} for significance, {drift} observed run-to-run drift).")
elif net > 0:
print(f" CANDIDATE BETTER by {net} queries (p={p:.4f}), outside the noise band "
f"(>= {floor}).")
else:
print(f" CANDIDATE WORSE by {abs(net)} queries (p={p:.4f}), outside the noise band "
f"(>= {floor}).")
if args.out:
with open(args.out, "w", encoding="utf-8") as fh:
json.dump({
"baseline": A["label"], "candidate": B["label"],
"n_shared_queries": n,
"fixed_by_candidate": fixed, "broken_by_candidate": broken,
"discordant": b + c, "net_queries": net,
"mcnemar_exact_p": p,
"min_detectable_swing_queries": mds,
"observed_run_to_run_drift_queries": drift,
"noise_floor_queries": floor,
"verdict": ("no measurable difference" if abs(net) < floor
else ("candidate better" if net > 0 else "candidate worse")),
"baseline_aggregate": aa, "candidate_aggregate": ab,
"repeat_variance": var,
}, fh, indent=1)
print(f"\nwrote {args.out}")
if __name__ == "__main__":
main()
tools/retrieval-eval/comparison-main-vs-recall-through-activation.json
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{
"baseline": "main-r1",
"candidate": "act-r1",
"n_shared_queries": 38,
"fixed_by_candidate": [],
"broken_by_candidate": [
"q07",
"q11",
"q12",
"q13",
"q36"
],
"discordant": 5,
"net_queries": -5,
"mcnemar_exact_p": 0.0625,
"min_detectable_swing_queries": 6,
"observed_run_to_run_drift_queries": 1,
"noise_floor_queries": 6,
"verdict": "no measurable difference",
"baseline_aggregate": {
"n_queries": 38,
"n_scored": 35,
"hit@5": 0.34285714285714286,
"recall@5": 0.26947278911564626,
"recall@10": 0.3333333333333333,
"precision@5": 0.12000000000000001,
"mrr@10": 0.2943197278911564,
"nonsense_clean": "2/3",
"superseded_outranks": "1/3",
"latency_ms_p50": 1140.4,
"latency_ms_p95": 1584.1,
"latency_ms_max": 1627.6,
"errors": 0,
"by_category": {
"associative": {
"n": 6,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"mrr@10": 0.0
},
"exact_rare": {
"n": 6,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"mrr@10": 1.0
},
"nonsense": {
"n": 3,
"clean": 2,
"avg_false_positives": 3.3333333333333335
},
"paraphrase": {
"n": 13,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"mrr@10": 0.0
},
"phrase": {
"n": 7,
"hit@5": 0.8571428571428571,
"recall@5": 0.4902210884353741,
"recall@10": 0.6666666666666666,
"mrr@10": 0.5965986394557822
},
"superseded": {
"n": 3,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.3333333333333333,
"mrr@10": 0.041666666666666664,
"outranks": 1
}
}
},
"candidate_aggregate": {
"n_queries": 38,
"n_scored": 35,
"hit@5": 0.22857142857142856,
"recall@5": 0.19087301587301586,
"recall@10": 0.24277210884353742,
"precision@5": 0.07428571428571429,
"mrr@10": 0.24154195011337865,
"nonsense_clean": "2/3",
"superseded_outranks": "0/3",
"latency_ms_p50": 3208.8,
"latency_ms_p95": 4851.9,
"latency_ms_max": 5078.8,
"errors": 0,
"by_category": {
"associative": {
"n": 6,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"mrr@10": 0.0
},
"exact_rare": {
"n": 6,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"mrr@10": 1.0
},
"nonsense": {
"n": 3,
"clean": 2,
"avg_false_positives": 2.6666666666666665
},
"paraphrase": {
"n": 13,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"mrr@10": 0.0
},
"phrase": {
"n": 7,
"hit@5": 0.2857142857142857,
"recall@5": 0.09722222222222222,
"recall@10": 0.3567176870748299,
"mrr@10": 0.3505668934240363
},
"superseded": {
"n": 3,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"mrr@10": 0.0,
"outranks": 0
}
}
},
"repeat_variance": {
"baseline": {
"runs": 3,
"hit@5_min": 0.34285714285714286,
"hit@5_max": 0.34285714285714286,
"spread_queries": 0
},
"candidate": {
"runs": 3,
"hit@5_min": 0.22857142857142856,
"hit@5_max": 0.2571428571428571,
"spread_queries": 1
}
}
}
tools/retrieval-eval/gold_set.json
+587
View File
@@ -0,0 +1,587 @@
{
"corpus": "/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json",
"corpus_nodes": 78768,
"corpus_edges": 14214,
"note": "Every query carries a `derivation` recording how its expected answer was chosen. Re-run with --check to re-validate the whole set against the corpus.",
"queries": [
{
"id": "q01",
"category": "exact_rare",
"query": "unjailbreakable",
"relevant": [
"mem-7f61beb4-271c-4feb-9f6e-1c9c837a6226"
],
"derivation": "MINED: token 'unjailbreakable' has document frequency 1 over all 78768 corpus nodes (re-verified at build time). Its single containing node is mem-7f61beb4-271c-4feb-9f6e-1c9c837a6226 ('Daemon hidden substrate architecture ? implemented April 25 '), which is therefore the only possible correct answer."
},
{
"id": "q02",
"category": "exact_rare",
"query": "engram-migrate",
"relevant": [
"mem-6fdf6545-5e1a-43a9-8bdc-d2cd248146a5"
],
"derivation": "MINED: token 'engram-migrate' has document frequency 1 over all 78768 corpus nodes (re-verified at build time). Its single containing node is mem-6fdf6545-5e1a-43a9-8bdc-d2cd248146a5 ('Engram v0.1 complete ? April 27, 2026. Local-first spreading'), which is therefore the only possible correct answer."
},
{
"id": "q03",
"category": "exact_rare",
"query": "cartabandonedevent",
"relevant": [
"mem-1ba7c67d-85b9-4c2e-9fe2-39f8b0477091"
],
"derivation": "MINED: token 'cartabandonedevent' has document frequency 1 over all 78768 corpus nodes (re-verified at build time). Its single containing node is mem-1ba7c67d-85b9-4c2e-9fe2-39f8b0477091 ('MESSAGE FOR AUDIT AGENT af5a7352e70e80434 ? El Language Spec'), which is therefore the only possible correct answer."
},
{
"id": "q04",
"category": "exact_rare",
"query": "pre-apprenticeship",
"relevant": [
"mem-89c02aae-d3ca-43f9-9e5d-eb369896276c"
],
"derivation": "MINED: token 'pre-apprenticeship' has document frequency 1 over all 78768 corpus nodes (re-verified at build time). Its single containing node is mem-89c02aae-d3ca-43f9-9e5d-eb369896276c ('William Fox Anderson ? Applicant Profile Personal: - Full N'), which is therefore the only possible correct answer."
},
{
"id": "q05",
"category": "exact_rare",
"query": "inferencenodemanager",
"relevant": [
"mem-73969486-143f-4431-b5e6-6845d1cc9848"
],
"derivation": "MINED: token 'inferencenodemanager' has document frequency 1 over all 78768 corpus nodes (re-verified at build time). Its single containing node is mem-73969486-143f-4431-b5e6-6845d1cc9848 ('Soma inference backplane deployed April 28 2026. Architectur'), which is therefore the only possible correct answer."
},
{
"id": "q06",
"category": "exact_rare",
"query": "clear-eyed",
"relevant": [
"knw-c72597c5-c23d-4c08-8e9e-996dadf26a99"
],
"derivation": "MINED: token 'clear-eyed' has document frequency 1 over all 78768 corpus nodes (re-verified at build time). Its single containing node is knw-c72597c5-c23d-4c08-8e9e-996dadf26a99 ('Clear Eyes ? The Incomplete World View'), which is therefore the only possible correct answer."
},
{
"id": "q07",
"category": "phrase",
"query": "patterns not returns",
"relevant": [
"mem-a4a9dfc3-e40b-49b3-b1e1-060e8be2f482"
],
"derivation": "MINED: a verbatim correction Will issued; expected = every node containing the phrase. Case-insensitive verbatim substring scan over label+content+tags at build time returns exactly 1 node(s); that set IS the answer key."
},
{
"id": "q08",
"category": "phrase",
"query": "thirty moves",
"relevant": [
"766de879-f9d0-4a07-b6df-b43ee13763d8",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"??o?'?B???k",
"Kp???",
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
"kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"kn-6061318f-046b-4935-907d-8eafdce14930",
"kn-f230b362-b201-4402-9833-4160c89ab3d4",
"knw-2c46cfb4-6d4e-4822-8a1a-7d743c1e4329",
"knw-4aebd815-4eaf-49d7-954b-03595f3d48be",
"knw-7902acca-604e-409b-8faf-ad85424211d0",
"knw-e94982a2-358d-4f2f-af31-8ee0fcec07c6",
"knw-f671966c-3387-4848-abca-b5deec122e00",
"ע?RGk?\tH(?"
],
"derivation": "MINED: the canonical biographical phrase; expected = every node containing it. Case-insensitive verbatim substring scan over label+content+tags at build time returns exactly 16 node(s); that set IS the answer key."
},
{
"id": "q09",
"category": "phrase",
"query": "Grandma Lucas",
"relevant": [
"766de879-f9d0-4a07-b6df-b43ee13763d8",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"art-0f0277a1-4a8e-4645-95dd-fa379976f31c",
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e"
],
"derivation": "MINED: a named person appearing verbatim in the biography/value nodes. Case-insensitive verbatim substring scan over label+content+tags at build time returns exactly 9 node(s); that set IS the answer key."
},
{
"id": "q10",
"category": "phrase",
"query": "Directed Harmonic",
"relevant": [
"%???2??jH??",
"63307ac5-cf6b-46e0-8296-07503b461cfa",
"7c9d4ab1-205d-4be8-bfae-e2c03a3a5010",
"9f291d20-0d32-413c-8c01-4416ccab4f7f",
"?;????n}rh?",
"???Ͼd??f??",
"?Z?.\f?0?]P?",
"Dp???]Q??k+",
"mem-7eeacad7-d7c2-4c2b-8348-19a59aa6dbaf"
],
"derivation": "MINED: the canonical DHARMA expansion, confirmed by Will April 24 2026. Case-insensitive verbatim substring scan over label+content+tags at build time returns exactly 9 node(s); that set IS the answer key."
},
{
"id": "q11",
"category": "phrase",
"query": "Sarah Bishop",
"relevant": [
"766de879-f9d0-4a07-b6df-b43ee13763d8",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e"
],
"derivation": "MINED: a named person; rare enough that the answer set is unambiguous. Case-insensitive verbatim substring scan over label+content+tags at build time returns exactly 2 node(s); that set IS the answer key."
},
{
"id": "q12",
"category": "phrase",
"query": "Directed Autonomous Runtime Modification",
"relevant": [
"2a923500-d7e1-4b15-80e2-48dba65984ba",
"7?e?7???\f3?",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"?of?7???",
"bl-145a0985-2382-400f-a7c5-c335c5e30a72",
"bl-5b17bd3b-0c41-46cb-a710-6fa4429692ff",
"g?2睪A|?H\b",
"mem-82b93b21-a865-410f-9ec1-fc54121d9bb5",
"mem-e6327f52-2bda-4ce7-9471-2fffd1e172de"
],
"derivation": "MINED: the DARMA expansion, quoted verbatim in the backlog item and its correction. Case-insensitive verbatim substring scan over label+content+tags at build time returns exactly 14 node(s); that set IS the answer key."
},
{
"id": "q13",
"category": "phrase",
"query": "zero-knowledge encrypted backup",
"relevant": [
"deda48cd-5e1a-46cb-bd43-8016afdb3a8a"
],
"derivation": "MINED: the paid-tier feature name as written in the roadmap nodes. Case-insensitive verbatim substring scan over label+content+tags at build time returns exactly 1 node(s); that set IS the answer key."
},
{
"id": "q14",
"category": "paraphrase",
"query": "the elderly relative who passed while he stayed away",
"relevant": [
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Do the Essential Thing While You Can', whose subject is Grandma Lucas dying in Feb 2006 without Will saying goodbye. Query names the event with none of the node's own vocabulary. VERIFIED at build time: of the 5 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"away",
"elderly",
"passed",
"relative",
"stayed"
]
},
{
"id": "q15",
"category": "paraphrase",
"query": "a soldier sidelined by illness who refused to quit",
"relevant": [
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Survival Is Not an Excuse to Stop', whose subject is enlisting in the Marines, a severe hernia, and sepsis. Query describes the episode obliquely. VERIFIED at build time: of the 5 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"illness",
"quit",
"refused",
"sidelined",
"soldier"
]
},
{
"id": "q16",
"category": "paraphrase",
"query": "choosing an uncomfortable fact over a pleasant fiction",
"relevant": [
"kn-13f60407-7b70-4db1-964f-ea1f8196efbd"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Honesty Before Comfort'. Query states the principle in wholly different words. VERIFIED at build time: of the 5 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"choosing",
"fact",
"fiction",
"pleasant",
"uncomfortable"
]
},
{
"id": "q17",
"category": "paraphrase",
"query": "a tight payload beats a bloated one",
"relevant": [
"kn-22d77abe-b3c5-42fd-afcd-dcb87d924929"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Precision Over Brute Force'. Query restates the claim with no shared vocabulary. VERIFIED at build time: of the 4 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"beats",
"bloated",
"payload",
"tight"
]
},
{
"id": "q18",
"category": "paraphrase",
"query": "if you are able and nobody is coming the job is yours",
"relevant": [
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Capability Is a Debt You Owe the Moment'. Query states the obligation without the node's terms. VERIFIED at build time: of the 4 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"able",
"coming",
"job",
"nobody"
]
},
{
"id": "q19",
"category": "paraphrase",
"query": "learning is the wealth creditors cannot seize",
"relevant": [
"kn-0bb4f021-56de-4947-a35b-a37209e7ba21"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Knowledge Survives When Nothing Else Does', whose subject is the library following Will across 30+ moves. VERIFIED at build time: of the 4 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"creditors",
"learning",
"seize",
"wealth"
]
},
{
"id": "q20",
"category": "paraphrase",
"query": "reliability proven by track record not assertion",
"relevant": [
"kn-5de5a9ac-fd15-45ab-bf18-77566781cf40"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Earned Trust' ('Trust is demonstrated, not declared'). VERIFIED at build time: of the 5 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"assertion",
"proven",
"record",
"reliability",
"track"
]
},
{
"id": "q21",
"category": "paraphrase",
"query": "boundaries that enable instead of confine",
"relevant": [
"kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Constraints as Freedom'. Query is a restatement of the same claim. VERIFIED at build time: of the 4 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"boundaries",
"confine",
"enable",
"instead"
]
},
{
"id": "q22",
"category": "paraphrase",
"query": "what shifts tells you where to cut a system apart",
"relevant": [
"kn-78db5396-3dbc-4481-bfc7-e4e1422feb1c"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Change Is the Signal', the value VBD is built on. VERIFIED at build time: of the 5 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"apart",
"cut",
"shifts",
"system",
"tells"
]
},
{
"id": "q23",
"category": "paraphrase",
"query": "a mind that compounds instead of resetting each day",
"relevant": [
"kn-f230b362-b201-4402-9833-4160c89ab3d4"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - The System Must Accumulate'. Query is the accumulation claim in different vocabulary. VERIFIED at build time: of the 5 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"compounds",
"day",
"instead",
"mind",
"resetting"
]
},
{
"id": "q24",
"category": "paraphrase",
"query": "loved for the unedited self and not the polished exterior",
"relevant": [
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Being Seen Is Rarer Than Being Known', whose subject is Sarah Bishop as the first person Will did not perform for. VERIFIED at build time: of the 5 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"exterior",
"loved",
"polished",
"self",
"unedited"
]
},
{
"id": "q25",
"category": "paraphrase",
"query": "cheerfulness you arrive at instead of assuming",
"relevant": [
"kn-e0423482-cfa5-4796-8689-8495c93b66bc"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Hope Is a Conclusion'. Query restates 'a conclusion, not a premise'. VERIFIED at build time: of the 4 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"arrive",
"assuming",
"cheerfulness",
"instead"
]
},
{
"id": "q26",
"category": "paraphrase",
"query": "a childhood offering no solid foundation to inherit",
"relevant": [
"kn-6061318f-046b-4935-907d-8eafdce14930"
],
"derivation": "HAND-SELECTED with criterion: target: 'Value - Structure Is Not Inherited', whose subject is thirty moves between two parents' collapses. VERIFIED at build time: of the 5 content words in the query, ZERO appear anywhere in the target's label, content, or tags — so no string-matching retriever can reach this answer.",
"zero_overlap_verified": true,
"query_content_words": [
"childhood",
"foundation",
"inherit",
"offering",
"solid"
]
},
{
"id": "q27",
"category": "associative",
"query": "Grandma Lucas stroke February 2006 goodbye window",
"relevant": [
"kn-0bb4f021-56de-4947-a35b-a37209e7ba21",
"kn-13f60407-7b70-4db1-964f-ea1f8196efbd",
"kn-22d77abe-b3c5-42fd-afcd-dcb87d924929",
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee",
"kn-5de5a9ac-fd15-45ab-bf18-77566781cf40",
"kn-6061318f-046b-4935-907d-8eafdce14930",
"kn-78db5396-3dbc-4481-bfc7-e4e1422feb1c",
"kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83",
"kn-e0423482-cfa5-4796-8689-8495c93b66bc",
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
"kn-f230b362-b201-4402-9833-4160c89ab3d4"
],
"derivation": "DERIVED FROM EDGES: the query is built from the distinctive vocabulary of kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71 ('Value ? Do the Essential Thing While You Can'), which hangs off the values hub kn-5b606390-a52d-4ca2-8e0e-eba141d13440 by an `identity` edge. Expected answers are that node's SIBLINGS on the same hub (11 of 13; 2 dropped because they shared a query word and so were lexically reachable). Every remaining sibling shares ZERO content words with the query — the only route from query to answer is seed(kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71) -> hub -> sibling, a two-hop traversal.",
"associative_source": "kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"hub": "kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"siblings_dropped_for_overlap": 2
},
{
"id": "q28",
"category": "associative",
"query": "Marines hernia sepsis medical ward",
"relevant": [
"015644f5-8194-4af0-800d-dd4a0cd71396",
"kn-0bb4f021-56de-4947-a35b-a37209e7ba21",
"kn-13f60407-7b70-4db1-964f-ea1f8196efbd",
"kn-22d77abe-b3c5-42fd-afcd-dcb87d924929",
"kn-5de5a9ac-fd15-45ab-bf18-77566781cf40",
"kn-6061318f-046b-4935-907d-8eafdce14930",
"kn-78db5396-3dbc-4481-bfc7-e4e1422feb1c",
"kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83",
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"kn-e0423482-cfa5-4796-8689-8495c93b66bc",
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
"kn-f230b362-b201-4402-9833-4160c89ab3d4"
],
"derivation": "DERIVED FROM EDGES: the query is built from the distinctive vocabulary of kn-58874a74-b96f-4883-9e08-45707f4bd3ee ('Value ? Survival Is Not an Excuse to Stop'), which hangs off the values hub kn-5b606390-a52d-4ca2-8e0e-eba141d13440 by an `identity` edge. Expected answers are that node's SIBLINGS on the same hub (13 of 13; 0 dropped because they shared a query word and so were lexically reachable). Every remaining sibling shares ZERO content words with the query — the only route from query to answer is seed(kn-58874a74-b96f-4883-9e08-45707f4bd3ee) -> hub -> sibling, a two-hop traversal.",
"associative_source": "kn-58874a74-b96f-4883-9e08-45707f4bd3ee",
"hub": "kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"siblings_dropped_for_overlap": 0
},
{
"id": "q29",
"category": "associative",
"query": "Sarah Bishop Dyer trailer performance",
"relevant": [
"015644f5-8194-4af0-800d-dd4a0cd71396",
"kn-0bb4f021-56de-4947-a35b-a37209e7ba21",
"kn-13f60407-7b70-4db1-964f-ea1f8196efbd",
"kn-22d77abe-b3c5-42fd-afcd-dcb87d924929",
"kn-5de5a9ac-fd15-45ab-bf18-77566781cf40",
"kn-6061318f-046b-4935-907d-8eafdce14930",
"kn-78db5396-3dbc-4481-bfc7-e4e1422feb1c",
"kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83",
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"kn-e0423482-cfa5-4796-8689-8495c93b66bc",
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8"
],
"derivation": "DERIVED FROM EDGES: the query is built from the distinctive vocabulary of kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e ('Value ? Being Seen Is Rarer Than Being Known'), which hangs off the values hub kn-5b606390-a52d-4ca2-8e0e-eba141d13440 by an `identity` edge. Expected answers are that node's SIBLINGS on the same hub (11 of 13; 2 dropped because they shared a query word and so were lexically reachable). Every remaining sibling shares ZERO content words with the query — the only route from query to answer is seed(kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e) -> hub -> sibling, a two-hop traversal.",
"associative_source": "kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"hub": "kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"siblings_dropped_for_overlap": 2
},
{
"id": "q30",
"category": "associative",
"query": "Swarm Architecture containment lateral worker",
"relevant": [
"kn-0bb4f021-56de-4947-a35b-a37209e7ba21",
"kn-13f60407-7b70-4db1-964f-ea1f8196efbd",
"kn-5de5a9ac-fd15-45ab-bf18-77566781cf40",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"kn-e0423482-cfa5-4796-8689-8495c93b66bc",
"kn-f230b362-b201-4402-9833-4160c89ab3d4"
],
"derivation": "DERIVED FROM EDGES: the query is built from the distinctive vocabulary of kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83 ('Value ? Constraints as Freedom'), which hangs off the values hub kn-5b606390-a52d-4ca2-8e0e-eba141d13440 by an `identity` edge. Expected answers are that node's SIBLINGS on the same hub (6 of 13; 7 dropped because they shared a query word and so were lexically reachable). Every remaining sibling shares ZERO content words with the query — the only route from query to answer is seed(kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83) -> hub -> sibling, a two-hop traversal.",
"associative_source": "kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83",
"hub": "kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"siblings_dropped_for_overlap": 7
},
{
"id": "q31",
"category": "associative",
"query": "hope won inside the narrative preface",
"relevant": [
"kn-0bb4f021-56de-4947-a35b-a37209e7ba21",
"kn-22d77abe-b3c5-42fd-afcd-dcb87d924929",
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee",
"kn-5de5a9ac-fd15-45ab-bf18-77566781cf40",
"kn-6061318f-046b-4935-907d-8eafdce14930",
"kn-78db5396-3dbc-4481-bfc7-e4e1422feb1c",
"kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83",
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
"kn-f230b362-b201-4402-9833-4160c89ab3d4"
],
"derivation": "DERIVED FROM EDGES: the query is built from the distinctive vocabulary of kn-e0423482-cfa5-4796-8689-8495c93b66bc ('Value ? Hope Is a Conclusion'), which hangs off the values hub kn-5b606390-a52d-4ca2-8e0e-eba141d13440 by an `identity` edge. Expected answers are that node's SIBLINGS on the same hub (11 of 13; 2 dropped because they shared a query word and so were lexically reachable). Every remaining sibling shares ZERO content words with the query — the only route from query to answer is seed(kn-e0423482-cfa5-4796-8689-8495c93b66bc) -> hub -> sibling, a two-hop traversal.",
"associative_source": "kn-e0423482-cfa5-4796-8689-8495c93b66bc",
"hub": "kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"siblings_dropped_for_overlap": 2
},
{
"id": "q32",
"category": "associative",
"query": "man of the house six years old expectation",
"relevant": [
"015644f5-8194-4af0-800d-dd4a0cd71396",
"kn-0bb4f021-56de-4947-a35b-a37209e7ba21",
"kn-13f60407-7b70-4db1-964f-ea1f8196efbd",
"kn-22d77abe-b3c5-42fd-afcd-dcb87d924929",
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee",
"kn-5de5a9ac-fd15-45ab-bf18-77566781cf40",
"kn-78db5396-3dbc-4481-bfc7-e4e1422feb1c",
"kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83",
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"kn-e0423482-cfa5-4796-8689-8495c93b66bc"
],
"derivation": "DERIVED FROM EDGES: the query is built from the distinctive vocabulary of kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8 ('Value ? Capability Is a Debt You Owe the Moment'), which hangs off the values hub kn-5b606390-a52d-4ca2-8e0e-eba141d13440 by an `identity` edge. Expected answers are that node's SIBLINGS on the same hub (11 of 13; 2 dropped because they shared a query word and so were lexically reachable). Every remaining sibling shares ZERO content words with the query — the only route from query to answer is seed(kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8) -> hub -> sibling, a two-hop traversal.",
"associative_source": "kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
"hub": "kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"siblings_dropped_for_overlap": 2
},
{
"id": "q33",
"category": "nonsense",
"query": "zqxjvw plimforth grebulon",
"relevant": [],
"derivation": "CONTROL: verified at build time that none of this string's tokens occurs anywhere in the corpus. Correct behaviour is to return NOTHING; any result is a false positive.",
"expect_empty": true
},
{
"id": "q34",
"category": "nonsense",
"query": "flarnbistle quommetry",
"relevant": [],
"derivation": "CONTROL: verified at build time that none of this string's tokens occurs anywhere in the corpus. Correct behaviour is to return NOTHING; any result is a false positive.",
"expect_empty": true
},
{
"id": "q35",
"category": "nonsense",
"query": "xxqzzt vurblenacht throom",
"relevant": [],
"derivation": "CONTROL: verified at build time that none of this string's tokens occurs anywhere in the corpus. Correct behaviour is to return NOTHING; any result is a false positive.",
"expect_empty": true
},
{
"id": "q36",
"category": "superseded",
"query": "is the self-improvement architecture called DARMA or DHARMA",
"relevant": [
"mem-80d7416b-20e9-48a0-b176-b215527e2f56"
],
"derivation": "DERIVED: correction node is Will's confirmation that the H is intentional (DHARMA, not DARMA); the stale node is the surviving backlog item still titled 'Implement DARMA'. Scored on RANKING, not presence: the corrected node mem-80d7416b-20e9-48a0-b176-b215527e2f56 must be returned AND must rank above the stale node bl-5b17bd3b-0c41-46cb-a710-6fa4429692ff.",
"must_outrank": [
"mem-80d7416b-20e9-48a0-b176-b215527e2f56",
"bl-5b17bd3b-0c41-46cb-a710-6fa4429692ff"
],
"stale_id": "bl-5b17bd3b-0c41-46cb-a710-6fa4429692ff",
"correct_label": "CORRECTION: The autonomous self-improvement architecture is DHARMA ? n",
"stale_label": "Implement DARMA ? Directed Autonomous Runtime Modification Architectur"
},
{
"id": "q37",
"category": "superseded",
"query": "how many provisional patents does Will actually have",
"relevant": [
"3cf706a1-3825-45d8-b0a9-06cae6cdf5b8"
],
"derivation": "DERIVED: correction node is the 2026-06-17 confabulation flag establishing EXACTLY 6 provisionals; the stale node is the surviving memory that asserts 12 filed patents. Scored on RANKING, not presence: the corrected node 3cf706a1-3825-45d8-b0a9-06cae6cdf5b8 must be returned AND must rank above the stale node 936541a9-fabb-466b-9ca3-a78b17ad0c53.",
"must_outrank": [
"3cf706a1-3825-45d8-b0a9-06cae6cdf5b8",
"936541a9-fabb-466b-9ca3-a78b17ad0c53"
],
"stale_id": "936541a9-fabb-466b-9ca3-a78b17ad0c53",
"correct_label": "memory:remembered",
"stale_label": "memory:remembered"
},
{
"id": "q38",
"category": "superseded",
"query": "is MCP still the live integration layer",
"relevant": [
"mem-30425134-6008-4fd9-a3ee-67a7742c319b"
],
"derivation": "DERIVED: correction node is the 'CGI ARCHITECTURE - THREE LAYERS, MCP RETIRED' decision of April 30 2026; the stale node still records the MCP server as live. Scored on RANKING, not presence: the corrected node mem-30425134-6008-4fd9-a3ee-67a7742c319b must be returned AND must rank above the stale node mem-101e81b4-8097-4749-8d8d-7bb66de34517.",
"must_outrank": [
"mem-30425134-6008-4fd9-a3ee-67a7742c319b",
"mem-101e81b4-8097-4749-8d8d-7bb66de34517"
],
"stale_id": "mem-101e81b4-8097-4749-8d8d-7bb66de34517",
"correct_label": "CGI ARCHITECTURE ? THREE LAYERS, MCP RETIRED (April 30, 2026). Definit",
"stale_label": "GCloud MCP infrastructure ? April 27, 2026. Legion died (~19:30 UTC). "
}
]
}
tools/retrieval-eval/results-act-r2.json
+942
View File
@@ -0,0 +1,942 @@
{
"label": "act-r2",
"soul_binary": "/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad/soul-act",
"soul_md5": "77722f5a9f49494bf735c2a4be1b5dc4",
"corpus": "/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json",
"corpus_nodes": 78768,
"corpus_edges": 14214,
"gold_set": "/Users/timlingo/Development/neuron-technologies/_wt-eval/tools/retrieval-eval/gold_set.json",
"limit": 10,
"port": 7902,
"wall_clock_s": 121.2,
"child_pid": 78714,
"child_confirmed_dead": true,
"aggregate": {
"n_queries": 38,
"n_scored": 35,
"hit@5": 0.22857142857142856,
"recall@5": 0.18908730158730158,
"recall@10": 0.24277210884353742,
"precision@5": 0.06857142857142857,
"mrr@10": 0.24154195011337865,
"nonsense_clean": "2/3",
"superseded_outranks": "0/3",
"latency_ms_p50": 3237.6,
"latency_ms_p95": 5264.0,
"latency_ms_max": 5510.9,
"errors": 0,
"by_category": {
"associative": {
"n": 6,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"mrr@10": 0.0
},
"exact_rare": {
"n": 6,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"mrr@10": 1.0
},
"nonsense": {
"n": 3,
"clean": 2,
"avg_false_positives": 2.6666666666666665
},
"paraphrase": {
"n": 13,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"mrr@10": 0.0
},
"phrase": {
"n": 7,
"hit@5": 0.2857142857142857,
"recall@5": 0.0882936507936508,
"recall@10": 0.3567176870748299,
"mrr@10": 0.3505668934240363
},
"superseded": {
"n": 3,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"mrr@10": 0.0,
"outranks": 0
}
}
},
"rows": [
{
"id": "q01",
"category": "exact_rare",
"query": "unjailbreakable",
"returned": [
"mem-7f61beb4-271c-4feb-9f6e-1c9c837a6226"
],
"n_returned": 1,
"latency_ms": 476.5,
"error": null,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"precision@5": 0.2,
"mrr@10": 1.0
},
{
"id": "q02",
"category": "exact_rare",
"query": "engram-migrate",
"returned": [
"mem-6fdf6545-5e1a-43a9-8bdc-d2cd248146a5"
],
"n_returned": 1,
"latency_ms": 708.1,
"error": null,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"precision@5": 0.2,
"mrr@10": 1.0
},
{
"id": "q03",
"category": "exact_rare",
"query": "cartabandonedevent",
"returned": [
"mem-1ba7c67d-85b9-4c2e-9fe2-39f8b0477091"
],
"n_returned": 1,
"latency_ms": 532.0,
"error": null,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"precision@5": 0.2,
"mrr@10": 1.0
},
{
"id": "q04",
"category": "exact_rare",
"query": "pre-apprenticeship",
"returned": [
"mem-89c02aae-d3ca-43f9-9e5d-eb369896276c"
],
"n_returned": 1,
"latency_ms": 537.5,
"error": null,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"precision@5": 0.2,
"mrr@10": 1.0
},
{
"id": "q05",
"category": "exact_rare",
"query": "inferencenodemanager",
"returned": [
"mem-73969486-143f-4431-b5e6-6845d1cc9848"
],
"n_returned": 1,
"latency_ms": 568.7,
"error": null,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"precision@5": 0.2,
"mrr@10": 1.0
},
{
"id": "q06",
"category": "exact_rare",
"query": "clear-eyed",
"returned": [
"knw-c72597c5-c23d-4c08-8e9e-996dadf26a99"
],
"n_returned": 1,
"latency_ms": 555.4,
"error": null,
"hit@5": 1.0,
"recall@5": 1.0,
"recall@10": 1.0,
"precision@5": 0.2,
"mrr@10": 1.0
},
{
"id": "q07",
"category": "phrase",
"query": "patterns not returns",
"returned": [
"knw-d357b6bb-ad8a-4791-b516-426aea45fa5b",
"kn-69fd6e83-7718-4824-8d66-f49d8954e224",
"kn-d97920d0-1649-4223-9508-c0bb621e7fc0",
"kn-57b4c5e7-40c6-4c90-bf14-71841b0081d4",
"kn-c3d9d063-8c5d-45aa-900c-550914b2ff6d",
"kn-f838f113-76d5-4a15-9cef-14055c4723a3",
"bl-76e878aa-e1fe-468c-bf9c-854097cb7e0b",
"art-c71aef51-026f-4d63-80e9-2a0ec0dc3865",
"bl-e148d23c-24e8-4122-9915-d1c11f22052f",
"bl-31abf75b-998f-4a4f-a6dd-8204119e0451"
],
"n_returned": 10,
"latency_ms": 1906.2,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q08",
"category": "phrase",
"query": "thirty moves",
"returned": [
"knw-7902acca-604e-409b-8faf-ad85424211d0",
"bl-80720fdf-7ce7-4d28-aff8-21028d3a8cfb",
"art-2f29ad36-6ee6-4a0e-8d72-0eaf7d12d3a9",
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
"knw-ed33e669-0790-44cb-a036-958d605c6fea",
"kn-6061318f-046b-4935-907d-8eafdce14930",
"art-80ca3d31-84dc-4502-83f4-538372b9764f",
"knw-2c46cfb4-6d4e-4822-8a1a-7d743c1e4329",
"?Z?.\f?0?]P?",
"?Q??m?;��`'"
],
"n_returned": 10,
"latency_ms": 1103.3,
"error": null,
"hit@5": 1.0,
"recall@5": 0.0625,
"recall@10": 0.1875,
"precision@5": 0.2,
"mrr@10": 1.0
},
{
"id": "q09",
"category": "phrase",
"query": "Grandma Lucas",
"returned": [
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"? ?}&?#??X\b",
"art-0f0277a1-4a8e-4645-95dd-fa379976f31c",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"766de879-f9d0-4a07-b6df-b43ee13763d8",
"art-79042b8b-6192-440f-90b0-60708f7e6325"
],
"n_returned": 6,
"latency_ms": 1132.4,
"error": null,
"hit@5": 1.0,
"recall@5": 0.5555555555555556,
"recall@10": 0.5555555555555556,
"precision@5": 1.0,
"mrr@10": 1.0
},
{
"id": "q10",
"category": "phrase",
"query": "Directed Harmonic",
"returned": [
"bl-c9adb8e5-293f-4033-99f8-0405c17ef941",
"bl-7e7c3fdb-4132-487f-aa70-b2cd559cb7f0",
"bl-7aebe936-ac55-4f35-8932-adc5224ff854",
"bl-9d53422d-b703-4f1d-860a-8598cb29b792",
"mem-34f53a9d-a131-4f82-9dbd-b9eb4a9af52e",
"mem-60778715-758c-4677-933d-fc39b8f94152",
"? ?}&?#??X\b",
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
"knw-e24d6339-5ff3-4bed-ba53-707ffd0dc70a",
"mem-7eeacad7-d7c2-4c2b-8348-19a59aa6dbaf"
],
"n_returned": 10,
"latency_ms": 960.8,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.1111111111111111,
"precision@5": 0.0,
"mrr@10": 0.1
},
{
"id": "q11",
"category": "phrase",
"query": "Sarah Bishop",
"returned": [
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
";??A5???",
"art-94fae615-7cd5-4695-b968-977101b06a51",
"art-2fabd873-d787-49cb-ad30-d4ed9fcff8ef",
"mem-a9a9ce95-0d64-46eb-9db8-ff81d78ade35",
"mem-57164d5f-baf0-4149-957a-379a4e255d1a",
"? ?}&?#??X\b",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"art-8dbceb06-431a-416d-a723-e8c75d595154"
],
"n_returned": 10,
"latency_ms": 992.4,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.5,
"precision@5": 0.0,
"mrr@10": 0.1111111111111111
},
{
"id": "q12",
"category": "phrase",
"query": "Directed Autonomous Runtime Modification",
"returned": [
"kn-b2a99cd7-b379-4d9b-a996-e347a02c7bad",
"bl-a313d67b-dd6d-4e5b-a55a-03bc7bda17ae",
"kn-8e1bfb48-33a9-45ad-8da7-e0bdaa5d34e7",
"art-92e1837c-5919-42d0-bbb0-4d924d7b2864",
"bl-a7a1428f-db9c-417b-8e2c-713b1f84dc1f",
"mem-f823e835-313f-4282-b4b3-ce527ffc2f7a",
"? ?}&?#??X\b",
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
"?Z?.\f?0?]P?",
"bl-145a0985-2382-400f-a7c5-c335c5e30a72"
],
"n_returned": 10,
"latency_ms": 2820.6,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.14285714285714285,
"precision@5": 0.0,
"mrr@10": 0.14285714285714285
},
{
"id": "q13",
"category": "phrase",
"query": "zero-knowledge encrypted backup",
"returned": [
"art-e495c8c5-ad95-4b64-8771-f68aa4cfcd0a",
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356",
"bl-e20944e5-eb16-4ab3-a84d-111e0fc817fa",
"bl-e20944e5-f4a6-44a0-91b1-73d04ebed120",
"mem-47f72b5b-6e8b-4293-94f1-350197b4809a",
"mem-a5f04e52-91f8-41d2-af27-8bf803621758",
"7774a16c-1027-4e3b-a21e-67f1f95a4acd",
"? ?}&?#??X\b",
"7?e?7???\f3?",
"deda48cd-5e1a-46cb-bd43-8016afdb3a8a"
],
"n_returned": 10,
"latency_ms": 2609.4,
"error": null,
"hit@5": 0.0,
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tools/retrieval-eval/results-act-r3.json
+942
View File
@@ -0,0 +1,942 @@
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tools/retrieval-eval/results-feat-recall-through-activation.json
+942
View File
@@ -0,0 +1,942 @@
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tools/retrieval-eval/results-main-r2.json
+945
View File
@@ -0,0 +1,945 @@
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tools/retrieval-eval/results-main-r3.json
+945
View File
@@ -0,0 +1,945 @@
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tools/retrieval-eval/results-main.json
+945
View File
@@ -0,0 +1,945 @@
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"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q15",
"category": "paraphrase",
"query": "a soldier sidelined by illness who refused to quit",
"returned": [
"art-79042b8b-6192-440f-90b0-60708f7e6325",
"art-2fabd873-d787-49cb-ad30-d4ed9fcff8ef",
"art-2f29ad36-6ee6-4a0e-8d72-0eaf7d12d3a9",
"? ?}&?#??X\b",
"kn-a31e1001-342e-4deb-a2e6-6d02d1f22dee",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"knw-9e74ee95-ba7d-49b1-9262-977eae9729d1",
"? ?}&?#??X\b",
"? ?}&?#??X\b"
],
"n_returned": 10,
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"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q16",
"category": "paraphrase",
"query": "choosing an uncomfortable fact over a pleasant fiction",
"returned": [
"7?e?7???\f3?",
"? ?}&?#??X\b",
"kn-f8974b26-78a6-4aad-b893-19a73b20013d",
"?of?7???",
"????7???Ջ3",
"kn-d97920d0-1649-4223-9508-c0bb621e7fc0",
"kn-57b4c5e7-40c6-4c90-bf14-71841b0081d4",
"kn-9397c74b-35f3-4428-b4b0-5123353bbcd1",
"kn-81c24d13-a73b-4767-819c-dafaacc1498e",
"? ?}&?#??X\b"
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"n_returned": 10,
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"precision@5": 0.0,
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},
{
"id": "q17",
"category": "paraphrase",
"query": "a tight payload beats a bloated one",
"returned": [
"kn-d7c1e0fb-fa59-46d3-b4c9-a0d1d437a491",
"7?e?7???\f3?",
"? ?}&?#??X\b",
"kn-e8423822-eacf-4029-aa7b-10d4d28d621e",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"??o?'?B???k",
"kn-9397c74b-35f3-4428-b4b0-5123353bbcd1",
"? ?}&?#??X\b",
"?of?7???"
],
"n_returned": 10,
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"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q18",
"category": "paraphrase",
"query": "if you are able and nobody is coming the job is yours",
"returned": [
"7?e?7???\f3?",
"?of?7???",
"imp-dce1da0f-8776-4a9e-972b-33411a7ca138",
"kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"kn-83bb86c6-521d-416c-a86e-6e29c2d8f102",
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"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b"
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"n_returned": 9,
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"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q19",
"category": "paraphrase",
"query": "learning is the wealth creditors cannot seize",
"returned": [
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356",
"knw-7902acca-604e-409b-8faf-ad85424211d0",
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"kn-57b4c5e7-40c6-4c90-bf14-71841b0081d4",
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
"7?e?7???\f3?",
"??o?'?B???k",
"? ?}&?#??X\b",
"kn-0625e393-067c-4bba-8389-7e1b79265142",
"ע?RGk?\tH(?"
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"n_returned": 10,
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},
{
"id": "q20",
"category": "paraphrase",
"query": "reliability proven by track record not assertion",
"returned": [
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
"7?e?7???\f3?",
"?of?7???",
"art-2fabd873-d787-49cb-ad30-d4ed9fcff8ef",
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356"
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"n_returned": 10,
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"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q21",
"category": "paraphrase",
"query": "boundaries that enable instead of confine",
"returned": [
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"??????X??2c",
"ԍ????X????",
"dR????X?-?S",
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356",
"dz????Xƹ?i",
"kn-d7c1e0fb-fa59-46d3-b4c9-a0d1d437a491"
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"n_returned": 10,
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"error": null,
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},
{
"id": "q22",
"category": "paraphrase",
"query": "what shifts tells you where to cut a system apart",
"returned": [
"7?e?7???\f3?",
"kn-9397c74b-35f3-4428-b4b0-5123353bbcd1",
"?of?7???",
"kn-f8974b26-78a6-4aad-b893-19a73b20013d",
"knw-9e74ee95-ba7d-49b1-9262-977eae9729d1",
"dR????X?-?S",
"dz????Xƹ?i",
"ԍ????X????",
"??????X??2c",
"kn-d7c1e0fb-fa59-46d3-b4c9-a0d1d437a491"
],
"n_returned": 10,
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"error": null,
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},
{
"id": "q23",
"category": "paraphrase",
"query": "a mind that compounds instead of resetting each day",
"returned": [
"????7???Ջ3",
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356",
"?ǚ?7??????",
"7?e?7???\f3?",
"??f?7???",
"? ?}&?#??X\b",
"%???2??jH??",
"kn-e8423822-eacf-4029-aa7b-10d4d28d621e",
"? ?}&?#??X\b",
"kn-9397c74b-35f3-4428-b4b0-5123353bbcd1"
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},
{
"id": "q24",
"category": "paraphrase",
"query": "loved for the unedited self and not the polished exterior",
"returned": [
"mem-bbb126a1-b297-42bb-86be-796871829c94",
"mem-45022957-2d78-48aa-a714-16d6eca52e0f",
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
"knw-35940684-abc4-42f0-b942-818f66b1f69a",
"art-79042b8b-6192-440f-90b0-60708f7e6325",
"art-0f0277a1-4a8e-4645-95dd-fa379976f31c",
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
"7?e?7???\f3?",
"? ?}&?#??X\b",
"'?T?a\"B~-?8"
],
"n_returned": 10,
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"error": null,
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},
{
"id": "q25",
"category": "paraphrase",
"query": "cheerfulness you arrive at instead of assuming",
"returned": [
"015644f5-8194-4af0-800d-dd4a0cd71396",
"?ǚ?7??????",
"art-79042b8b-6192-440f-90b0-60708f7e6325",
"7?e?7???\f3?",
"kn-e8423822-eacf-4029-aa7b-10d4d28d621e",
"??S?7???",
"? ?}&?#??X\b",
"??f?7???",
"??S?7???",
"knw-5578cb21-e899-4822-b7f4-0d96fa094e3d"
],
"n_returned": 10,
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"error": null,
"hit@5": 0.0,
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"precision@5": 0.0,
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},
{
"id": "q26",
"category": "paraphrase",
"query": "a childhood offering no solid foundation to inherit",
"returned": [
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
"7?e?7???\f3?",
"kn-e8423822-eacf-4029-aa7b-10d4d28d621e",
"? ?}&?#??X\b",
"??o?'?B???k",
"?of?7???",
"? ?}&?#??X\b"
],
"n_returned": 10,
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"error": null,
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},
{
"id": "q27",
"category": "associative",
"query": "Grandma Lucas stroke February 2006 goodbye window",
"returned": [
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"art-79042b8b-6192-440f-90b0-60708f7e6325",
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
"?ǚ?7??????"
],
"n_returned": 10,
"latency_ms": 1416.3,
"error": null,
"hit@5": 0.0,
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"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q28",
"category": "associative",
"query": "Marines hernia sepsis medical ward",
"returned": [
"art-79042b8b-6192-440f-90b0-60708f7e6325",
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee",
"bl-33ecccc2-e37f-43db-91b3-c2a86f08aaac",
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
"art-e0bdf5d8-d163-491f-b649-453fee8b721d",
"7?e?7???\f3?",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"kn-9397c74b-35f3-4428-b4b0-5123353bbcd1",
"?of?7???"
],
"n_returned": 10,
"latency_ms": 1090.3,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q29",
"category": "associative",
"query": "Sarah Bishop Dyer trailer performance",
"returned": [
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
"7?e?7???\f3?",
"?of?7???",
"art-79042b8b-6192-440f-90b0-60708f7e6325",
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee"
],
"n_returned": 10,
"latency_ms": 1167.7,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q30",
"category": "associative",
"query": "Swarm Architecture containment lateral worker",
"returned": [
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
"bl-9bde67c1-f0ba-4c3a-8fe5-de0deee0ce43",
"8cbb60c5-4999-4ec1-8682-2592aedc4249",
"bl-0fac287f-f4c0-4f15-bc4d-ff7f8a7af3ae",
"7?e?7???\f3?",
"kn-6f248a50-355b-47bb-aec8-e0e646a9b077",
"? ?}&?#??X\b",
"h??I?cB?Q??",
"?of?7???",
"? ?}&?#??X\b"
],
"n_returned": 10,
"latency_ms": 1140.4,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q31",
"category": "associative",
"query": "hope won inside the narrative preface",
"returned": [
"kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
"kn-e0423482-cfa5-4796-8689-8495c93b66bc",
"knw-9e74ee95-ba7d-49b1-9262-977eae9729d1",
"7?e?7???\f3?",
"rQ??m?;?x?'",
"?Q??m?;?u?'",
"R^??m?;��?'",
"?Q??m?;��`'",
"? ?}&?#??X\b"
],
"n_returned": 9,
"latency_ms": 1157.6,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q32",
"category": "associative",
"query": "man of the house six years old expectation",
"returned": [
"? ?}&?#??X\b",
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"kn-57b4c5e7-40c6-4c90-bf14-71841b0081d4",
"knw-35940684-abc4-42f0-b942-818f66b1f69a",
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
"art-2fabd873-d787-49cb-ad30-d4ed9fcff8ef"
],
"n_returned": 10,
"latency_ms": 1379.7,
"error": null,
"hit@5": 0.0,
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"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0
},
{
"id": "q33",
"category": "nonsense",
"query": "zqxjvw plimforth grebulon",
"returned": [],
"n_returned": 0,
"latency_ms": 677.2,
"error": null,
"clean": true,
"false_positives": 0
},
{
"id": "q34",
"category": "nonsense",
"query": "flarnbistle quommetry",
"returned": [],
"n_returned": 0,
"latency_ms": 443.2,
"error": null,
"clean": true,
"false_positives": 0
},
{
"id": "q35",
"category": "nonsense",
"query": "xxqzzt vurblenacht throom",
"returned": [
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
"knw-920c891f-bb8c-48c4-9afc-018ef12dcdc4",
"art-79042b8b-6192-440f-90b0-60708f7e6325",
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
"bl-4476e856-c567-4b49-8ff7-d7dca3e5715e",
"kn-66a21179-2adc-4b19-a109-880cf4674d7d",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b"
],
"n_returned": 10,
"latency_ms": 679.8,
"error": null,
"clean": false,
"false_positives": 10
},
{
"id": "q36",
"category": "superseded",
"query": "is the self-improvement architecture called DARMA or DHARMA",
"returned": [
"7?e?7???\f3?",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"?of?7???",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"mem-80d7416b-20e9-48a0-b176-b215527e2f56",
"mem-f3b37427-b7d1-4f7e-b32c-0241a20ce8da",
"art-80ca3d31-84dc-4502-83f4-538372b9764f"
],
"n_returned": 10,
"latency_ms": 1245.4,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 1.0,
"precision@5": 0.0,
"mrr@10": 0.125,
"outranks": true,
"rank_correct": 8,
"rank_stale": null
},
{
"id": "q37",
"category": "superseded",
"query": "how many provisional patents does Will actually have",
"returned": [
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"? ?}&?#??X\b",
"7?e?7???\f3?",
"?of?7???",
"?ǚ?7??????",
"[?MO5????G",
"art-ee615cdb-e599-423d-9a4d-977859390ed3"
],
"n_returned": 10,
"latency_ms": 1618.4,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"precision@5": 0.0,
"mrr@10": 0.0,
"outranks": false,
"rank_correct": null,
"rank_stale": null
},
{
"id": "q38",
"category": "superseded",
"query": "is MCP still the live integration layer",
"returned": [
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356",
"7?e?7???\f3?",
"? ?}&?#??X\b",
"%???2??jH??",
"???Ͼd??W\b?",
"??o?'?B???k",
"? ?}&?#??X\b",
"63307ac5-cf6b-46e0-8296-07503b461cfa",
"? ?}&?#??X\b",
"?of?7???"
],
"n_returned": 10,
"latency_ms": 956.7,
"error": null,
"hit@5": 0.0,
"recall@5": 0.0,
"recall@10": 0.0,
"precision@5": 0.0,
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"outranks": false,
"rank_correct": null,
"rank_stale": null
}
]
}
tools/retrieval-eval/run_comparison.sh
Executable
+98
View File
@@ -0,0 +1,98 @@
#!/usr/bin/env bash
# run_comparison.sh — the whole harness, end to end, from two git refs.
#
# Builds a soul from each ref, boots each on its own throwaway port with its own
# throwaway HOME and its own disposable copy of the corpus, runs the gold set N
# times per ref, and prints the comparison with its noise threshold.
#
# SAFETY: never touches ~/.neuron, /Applications/Neuron*, ~/neuron-dev-stack, or
# any running service. Sources are exported with `git archive` into a scratch
# dir, so no worktree or branch state is mutated either. Ports are checked
# against the live set before anything boots. Every soul this script starts is
# killed and confirmed dead by run_eval.py; the sweep at the end is a backstop.
#
# usage:
# run_comparison.sh [--baseline main] [--candidate feat/recall-through-activation]
# [--repeats 3] [--corpus <snapshot.json>] [--repo <path>]
set -euo pipefail
BASELINE="main"
CANDIDATE="feat/recall-through-activation"
REPEATS=3
CORPUS="$HOME/neuron-memory-backups/snapshot-pre-repair-20260806.json"
REPO="$HOME/Development/neuron"
BASE_PORT=7893
while [ $# -gt 0 ]; do
case "$1" in
--baseline) BASELINE="$2"; shift 2 ;;
--candidate) CANDIDATE="$2"; shift 2 ;;
--repeats) REPEATS="$2"; shift 2 ;;
--corpus) CORPUS="$2"; shift 2 ;;
--repo) REPO="$2"; shift 2 ;;
*) echo "unknown arg: $1" >&2; exit 2 ;;
esac
done
HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
WORK="$(mktemp -d "${TMPDIR:-/tmp}/retrieval-eval.XXXXXX")"
trap 'rm -rf "$WORK"' EXIT
[ -f "$CORPUS" ] || { echo "no corpus at $CORPUS" >&2; exit 2; }
echo "corpus: $CORPUS ($(du -h "$CORPUS" | cut -f1))"
slug() { printf '%s' "$1" | tr '/' '-'; }
build_ref() { # ref -> binary path
local ref="$1" out="$WORK/soul-$(slug "$1")"
local src="$WORK/src-$(slug "$1")"
mkdir -p "$src"
git -C "$REPO" archive "$ref" | tar -x -C "$src"
"$HERE/build-soul.sh" "$src" "$out" >&2
printf '%s' "$out"
}
echo "== building $BASELINE =="
BIN_A="$(build_ref "$BASELINE")"
echo "== building $CANDIDATE =="
BIN_B="$(build_ref "$CANDIDATE")"
echo "== validating the gold set against this corpus =="
python3 "$HERE/build_gold_set.py" "$CORPUS" --check
port=$BASE_PORT
run_one() { # binary label out
echo "== $2 =="
python3 "$HERE/run_eval.py" --soul "$1" --corpus "$CORPUS" --label "$2" \
--port "$port" --out "$3"
port=$((port + 1))
}
A_MAIN="$WORK/results-a-1.json"; B_MAIN="$WORK/results-b-1.json"
A_REP=(); B_REP=()
for i in $(seq 1 "$REPEATS"); do
a="$WORK/results-a-$i.json"; b="$WORK/results-b-$i.json"
run_one "$BIN_A" "$(slug "$BASELINE")-r$i" "$a"
run_one "$BIN_B" "$(slug "$CANDIDATE")-r$i" "$b"
[ "$i" -gt 1 ] && { A_REP+=("$a"); B_REP+=("$b"); }
done
cp "$A_MAIN" "$HERE/results-$(slug "$BASELINE").json"
cp "$B_MAIN" "$HERE/results-$(slug "$CANDIDATE").json"
echo
python3 "$HERE/compare.py" \
--baseline "$A_MAIN" --candidate "$B_MAIN" \
${A_REP[@]+--repeats-baseline "${A_REP[@]}"} \
${B_REP[@]+--repeats-candidate "${B_REP[@]}"} \
--out "$HERE/comparison-$(slug "$BASELINE")-vs-$(slug "$CANDIDATE").json"
# Backstop: run_eval.py kills and confirms its own child, but a crashed run
# could leak one. Leaving a soul running is how the live engine got squeezed.
STRAY=$(pgrep -f "$WORK/soul-" || true)
if [ -n "$STRAY" ]; then
echo "!! stray eval souls, killing: $STRAY" >&2
kill -9 $STRAY 2>/dev/null || true
fi
pgrep -f "$WORK/soul-" >/dev/null && { echo "!! STILL RUNNING" >&2; exit 5; }
echo "process check: no eval souls running"
tools/retrieval-eval/run_eval.py
Executable
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#!/usr/bin/env python3
"""
run_eval.py — measure one soul build's retrieval against the gold set.
WHAT IT MEASURES, AND WHY IT BOOTS A REAL SOUL
The point is Will's designed retrieval — spreading activation over the
weighted directed graph with four-factor multiplicative scoring — not a
Python re-implementation of it. A re-implementation would measure my
reading of the design; booting the compiled binary measures the design. So
this harness compiles the actual `soul.el` amalgam (build-soul.sh) and asks
it over HTTP, exactly as the MCP wrapper and the app do.
SAFETY — read this before changing anything here
* Boots on a THROWAWAY port with a THROWAWAY $HOME and a THROWAWAY COPY of
the corpus. Refuses to use 7770 / 8742 / 7779 / 17779 / 7771.
* ENGRAM_URL / SOUL_ENGRAM_URL are UNSET and SOUL_ISE_URL is pinned to a
dead port. This is not belt-and-braces: the periodic engram sync resolves
its source as env(SOUL_ISE_URL) -> state -> DEFAULT http://localhost:8742,
so leaving it unset makes an "isolated" run silently pull the operator's
LIVE brain. (Learned the hard way on 2026-08-03; see the same note in
scripts/verify-soul-contract.sh.)
* Every process this file starts is tracked and killed in a finally block,
then CONFIRMED dead by pid probe, and the confirmation is written into the
results file. A run that cannot confirm its child is dead exits non-zero.
* Activation is a STATEFUL read by design (patent claim 29: traversal
updates last-activation and increments activation counts). The corpus copy
is therefore per-run and disposable, and every run starts from a byte-
identical copy so two configurations see the same starting graph.
usage:
python3 run_eval.py --soul <binary> --corpus <snapshot.json> --label main \
[--port 7893] [--gold gold_set.json] [--limit 10] [--out results-main.json]
"""
import argparse
import json
import os
import shutil
import signal
import subprocess
import sys
import tempfile
import time
import urllib.error
import urllib.parse
import urllib.request
HERE = os.path.dirname(os.path.abspath(__file__))
FORBIDDEN_PORTS = {7770, 8742, 7779, 17779, 7771, 8080}
# ─────────────────────────────────────────────────────────────────────────────
# metrics
# ─────────────────────────────────────────────────────────────────────────────
def recall_at_k(returned, relevant, k):
if not relevant:
return None
return len(set(returned[:k]) & set(relevant)) / len(relevant)
def hit_at_k(returned, relevant, k):
if not relevant:
return None
return 1.0 if set(returned[:k]) & set(relevant) else 0.0
def precision_at_k(returned, relevant, k):
"""Fixed denominator k, as in docs/research/graphrag_eval/score.py.
Fixed denominator penalises an empty result and a page of junk equally,
which is what we want: a retriever that returns nothing is not 'precise'.
"""
if not relevant:
return None
return len(set(returned[:k]) & set(relevant)) / k
def mrr(returned, relevant, k):
if not relevant:
return None
rel = set(relevant)
for i, nid in enumerate(returned[:k], start=1):
if nid in rel:
return 1.0 / i
return 0.0
def mean(vals):
vals = [v for v in vals if v is not None]
return sum(vals) / len(vals) if vals else 0.0
def pct(vals):
return f"{100 * mean(vals):.1f}%"
# ─────────────────────────────────────────────────────────────────────────────
# soul lifecycle
# ─────────────────────────────────────────────────────────────────────────────
class Soul:
def __init__(self, binary, corpus, port, verbose=True):
if port in FORBIDDEN_PORTS:
raise SystemExit(f"REFUSING: port {port} is a live service port.")
self.binary = os.path.abspath(binary)
self.corpus = os.path.abspath(corpus)
self.port = port
self.verbose = verbose
self.home = None
self.proc = None
self.pid = None
self.log = None
self.confirmed_dead = None
@property
def base(self):
return f"http://127.0.0.1:{self.port}"
def start(self, boot_timeout=180):
self.home = tempfile.mkdtemp(prefix="retrieval-eval-home.")
snap = os.path.join(self.home, "corpus.json")
t0 = time.time()
shutil.copyfile(self.corpus, snap) # per-run disposable copy, never the source
self.log = os.path.join(self.home, "soul.log")
env = {k: v for k, v in os.environ.items()
if k not in ("ENGRAM_URL", "ENGRAM_API_KEY", "SOUL_ENGRAM_URL",
"ANTHROPIC_API_KEY", "NEURON_LLM_API_KEY", "SOUL_IDENTITY",
"SOUL_API_KEY")}
env.update({
"HOME": self.home,
"NEURON_PORT": str(self.port),
"SOUL_CGI_ID": f"ntn-retrieval-eval-{os.getpid()}",
"SOUL_ENGRAM_PATH": snap,
"NEURON_API_URL": "http://127.0.0.1:9", # dead port
"SOUL_ISE_URL": "http://127.0.0.1:9", # dead port — see SAFETY above
# Park the background loops for an hour so heartbeat/consolidation
# cannot mutate the graph between queries and make runs unrepeatable.
"SOUL_TICK_MS": "3600000",
"SOUL_HEARTBEAT_MS": "3600000",
"SOUL_REFRESH_MS": "3600000",
})
with open(self.log, "wb") as lf:
self.proc = subprocess.Popen([self.binary], env=env, stdout=lf, stderr=lf,
start_new_session=True)
self.pid = self.proc.pid
if self.verbose:
print(f" booted pid={self.pid} port={self.port} home={self.home}")
deadline = time.time() + boot_timeout
while time.time() < deadline:
if self.proc.poll() is not None:
raise RuntimeError(f"soul exited during boot: {self._log_tail()}")
rss = self._rss_kb()
if rss and rss > 6 * 1024 * 1024:
self.stop()
raise RuntimeError(f"soul RSS {rss}KB > 6GB — aborted")
try:
with urllib.request.urlopen(f"{self.base}/health", timeout=2) as r:
if r.status == 200:
if self.verbose:
print(f" healthy in {time.time() - t0:.1f}s, RSS={self._rss_kb()}KB")
return
except Exception:
pass
time.sleep(0.5)
self.stop()
raise RuntimeError(f"soul never healthy on {self.base}: {self._log_tail()}")
def _rss_kb(self):
try:
out = subprocess.run(["ps", "-o", "rss=", "-p", str(self.pid)],
capture_output=True, text=True, timeout=5).stdout.strip()
return int(out) if out else None
except Exception:
return None
def _log_tail(self, n=15):
try:
with open(self.log, encoding="utf-8", errors="replace") as fh:
return "\n".join(fh.read().splitlines()[-n:])
except Exception:
return "(no log)"
def recall(self, query, limit, timeout=60):
url = f"{self.base}/api/neuron/recall?query={urllib.parse.quote(query)}&limit={limit}"
t0 = time.perf_counter()
try:
with urllib.request.urlopen(url, timeout=timeout) as r:
raw = r.read().decode("utf-8", "replace")
ms = (time.perf_counter() - t0) * 1000
except Exception as exc:
return [], (time.perf_counter() - t0) * 1000, f"{type(exc).__name__}: {exc}"
try:
arr = json.loads(raw)
except Exception:
return [], ms, f"unparseable response ({len(raw)}B)"
if not isinstance(arr, list):
return [], ms, f"non-array response: {str(arr)[:120]}"
ids = [x.get("id") for x in arr if isinstance(x, dict) and x.get("id")]
return ids, ms, None
def stop(self):
"""Kill and CONFIRM. A test process that outlives its test is a bug."""
if self.pid is None:
self.confirmed_dead = True
return True
for sig in (signal.SIGTERM, signal.SIGKILL):
try:
os.kill(self.pid, sig)
except ProcessLookupError:
break
except Exception:
pass
for _ in range(20):
try:
os.kill(self.pid, 0)
except ProcessLookupError:
break
time.sleep(0.1)
else:
continue
break
try:
self.proc.wait(timeout=5)
except Exception:
pass
try:
os.kill(self.pid, 0)
self.confirmed_dead = False
except ProcessLookupError:
self.confirmed_dead = True
if self.verbose:
print(f" pid {self.pid}: {'CONFIRMED DEAD' if self.confirmed_dead else 'STILL ALIVE'}")
if self.home and os.path.isdir(self.home):
shutil.rmtree(self.home, ignore_errors=True)
return self.confirmed_dead
# ─────────────────────────────────────────────────────────────────────────────
# eval
# ─────────────────────────────────────────────────────────────────────────────
def evaluate(soul, gold, limit):
rows = []
for q in gold["queries"]:
ids, ms, err = soul.recall(q["query"], limit)
rel = q.get("relevant") or []
row = {
"id": q["id"],
"category": q["category"],
"query": q["query"],
"returned": ids,
"n_returned": len(ids),
"latency_ms": round(ms, 1),
"error": err,
}
if q.get("expect_empty"):
row["clean"] = (len(ids) == 0)
row["false_positives"] = len(ids)
else:
row["hit@5"] = hit_at_k(ids, rel, 5)
row["recall@5"] = recall_at_k(ids, rel, 5)
row["recall@10"] = recall_at_k(ids, rel, 10)
row["precision@5"] = precision_at_k(ids, rel, 5)
row["mrr@10"] = mrr(ids, rel, 10)
if q.get("must_outrank"):
correct, stale = q["must_outrank"]
ic = ids.index(correct) if correct in ids else None
istale = ids.index(stale) if stale in ids else None
# Correct must be present AND above the stale node. A run that
# returns neither is NOT a pass: the corrected fact is what the
# user needed.
row["outranks"] = (ic is not None) and (istale is None or ic < istale)
row["rank_correct"] = None if ic is None else ic + 1
row["rank_stale"] = None if istale is None else istale + 1
rows.append(row)
return rows
def aggregate(rows):
scored = [r for r in rows if "hit@5" in r]
nonsense = [r for r in rows if "clean" in r]
outrank = [r for r in rows if "outranks" in r]
lat = sorted(r["latency_ms"] for r in rows)
agg = {
"n_queries": len(rows),
"n_scored": len(scored),
"hit@5": mean([r["hit@5"] for r in scored]),
"recall@5": mean([r["recall@5"] for r in scored]),
"recall@10": mean([r["recall@10"] for r in scored]),
"precision@5": mean([r["precision@5"] for r in scored]),
"mrr@10": mean([r["mrr@10"] for r in scored]),
"nonsense_clean": f"{sum(1 for r in nonsense if r['clean'])}/{len(nonsense)}",
"superseded_outranks": f"{sum(1 for r in outrank if r['outranks'])}/{len(outrank)}",
"latency_ms_p50": lat[len(lat) // 2] if lat else 0,
"latency_ms_p95": lat[max(0, int(len(lat) * 0.95) - 1)] if lat else 0,
"latency_ms_max": lat[-1] if lat else 0,
"errors": sum(1 for r in rows if r["error"]),
"by_category": {},
}
cats = sorted({r["category"] for r in rows})
for c in cats:
cr = [r for r in rows if r["category"] == c]
if c == "nonsense":
agg["by_category"][c] = {
"n": len(cr),
"clean": sum(1 for r in cr if r["clean"]),
"avg_false_positives": mean([float(r["false_positives"]) for r in cr]),
}
else:
e = {
"n": len(cr),
"hit@5": mean([r.get("hit@5") for r in cr]),
"recall@5": mean([r.get("recall@5") for r in cr]),
"recall@10": mean([r.get("recall@10") for r in cr]),
"mrr@10": mean([r.get("mrr@10") for r in cr]),
}
if c == "superseded":
e["outranks"] = sum(1 for r in cr if r.get("outranks"))
agg["by_category"][c] = e
return agg
def print_table(label, agg):
print(f"\n=== {label} ===")
print(f" queries {agg['n_queries']} ({agg['n_scored']} scored + "
f"{agg['n_queries'] - agg['n_scored']} control) · errors {agg['errors']}")
print(f" {'hit@5':>12} {'recall@5':>10} {'recall@10':>10} {'prec@5':>9} {'MRR@10':>9}")
print(f" {pct([agg['hit@5']]):>12} {pct([agg['recall@5']]):>10} {pct([agg['recall@10']]):>10} "
f"{pct([agg['precision@5']]):>9} {agg['mrr@10']:>9.3f}")
print(f" nonsense clean {agg['nonsense_clean']} · superseded outranks {agg['superseded_outranks']}")
print(f" latency ms p50 {agg['latency_ms_p50']:.0f} · p95 {agg['latency_ms_p95']:.0f} "
f"· max {agg['latency_ms_max']:.0f}")
print(f"\n {'category':14} {'n':>3} {'hit@5':>8} {'recall@5':>9} {'recall@10':>10} {'MRR@10':>8}")
for c, e in agg["by_category"].items():
if c == "nonsense":
print(f" {c:14} {e['n']:>3} {'clean ' + str(e['clean']) + '/' + str(e['n']):>8}"
f"{'':>9} {'':>10} {'avg FP ' + format(e['avg_false_positives'], '.1f'):>8}")
else:
extra = f" outranks {e['outranks']}/{e['n']}" if "outranks" in e else ""
print(f" {c:14} {e['n']:>3} {pct([e['hit@5']]):>8} {pct([e['recall@5']]):>9} "
f"{pct([e['recall@10']]):>10} {e['mrr@10']:>8.3f}{extra}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--soul", required=True)
ap.add_argument("--corpus", required=True)
ap.add_argument("--label", required=True)
ap.add_argument("--gold", default=os.path.join(HERE, "gold_set.json"))
ap.add_argument("--port", type=int, default=7893)
ap.add_argument("--limit", type=int, default=10)
ap.add_argument("--out", default=None)
args = ap.parse_args()
with open(args.gold, encoding="utf-8") as fh:
gold = json.load(fh)
print(f"[{args.label}] soul={os.path.basename(args.soul)} "
f"corpus={os.path.basename(args.corpus)} gold={len(gold['queries'])}q limit={args.limit}")
soul = Soul(args.soul, args.corpus, args.port)
rows = []
started = time.time()
try:
soul.start()
rows = evaluate(soul, gold, args.limit)
finally:
dead = soul.stop()
agg = aggregate(rows)
print_table(args.label, agg)
out = args.out or os.path.join(HERE, f"results-{args.label}.json")
doc = {
"label": args.label,
"soul_binary": os.path.abspath(args.soul),
"soul_md5": subprocess.run(["md5", "-q", args.soul], capture_output=True,
text=True).stdout.strip(),
"corpus": os.path.abspath(args.corpus),
"corpus_nodes": gold.get("corpus_nodes"),
"corpus_edges": gold.get("corpus_edges"),
"gold_set": os.path.abspath(args.gold),
"limit": args.limit,
"port": args.port,
"wall_clock_s": round(time.time() - started, 1),
"child_pid": soul.pid,
"child_confirmed_dead": soul.confirmed_dead,
"aggregate": agg,
"rows": rows,
}
with open(out, "w", encoding="utf-8") as fh:
json.dump(doc, fh, indent=1, ensure_ascii=False)
print(f"\nwrote {out}")
if not dead:
print("FATAL: child process could not be confirmed dead", file=sys.stderr)
sys.exit(4)
if __name__ == "__main__":
main()