experiment-forensics

name	experiment-forensics
description	Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".
argument-hint	["paper-dir | repo-dir"]
allowed-tools	Bash(*), Read, Write, Grep, Glob, mcp__codex__codex

Experiment Forensics — are the reported results what the code computes?

Audit experiment integrity for: $ARGUMENTS (a paper-dir or repo-dir; use an ABSOLUTE path — it is referred to as TARGET below). Emit span-anchored experiment-forensics.findings.json.

🔒 Do not wrap this skill in /loop, /schedule, or CronCreate. It is verdict-bearing input — it proposes the findings the deterministic adjudicator turns into the report. Re-firing it on a wall-clock timer adds no signal: what unlocks new conclusions is a higher observability level (a repo / result files arriving → L2), not elapsed time. Schedule the external wait that precedes it — artifacts released → run once at the new level. (Mirrors ARIS's external-cadence doctrine.)

Adapted from ARIS experiment-audit (#57/#131), reframed for the reviewer side. The original audits your own experiment before you claim results; this audits a third party's submission. The crucial reframe: at L0/L1 (no code) these patterns are not decidable — they appear only as info-level "could-not-verify" signals. Code-level fraud requires L2. A PDF can never produce a fraud verdict.

Why this exists

LLM-driven research pipelines (and rushed human work) produce results that look computed but are not what the paper claims. The repeatable failure modes — ported from ARIS's experiment-integrity audit — are:

1. Fake ground truth — the eval "reference/target" is derived from model outputs and reported as performance, not as a labeled proxy. HP-FAKE-GT
2. Score self-normalization — a metric divided by the model's own max/min/ mean to approach 1.0; no raw score shown. HP-SELF-NORM
3. Phantom results — a paper number maps to a result file or metric key that does not exist (or a function never called). HP-PHANTOM-RESULT
4. Dead metric code — a metric defined in eval code, discussed in the paper, but never called / never present in any result file. HP-DEAD-METRIC
5. Scope inflation (verified) — "comprehensive/robust/SOTA" while the repo actually ran 1–2 datasets/seeds/configs. HP-SCOPE-INFLATE
6. Method drift (confirmed) — the method described differs from the method evaluated (A-lite, A+oracle, extra data, different backbone, test-time labels the method claims not to use). HP-METHOD-DRIFT
7. Synthesized-looking results — numbers across configs related by a too-clean arithmetic pattern ("不像跑出来的"). HP-SUSPICIOUS-REGULARITY
8. Placeholder / fake data in released code — the released code still ships placeholder/dummy/fake data (e.g. a # fake data for plotting annotation, a TODO: replace with real data, a hard-coded np.random.* array) and a reported figure/number is drawn from it rather than from a real run. HP-PLACEHOLDER-DATA (flag the checkable code marker; do not infer who wrote it)
9. Result ≠ artifact — the code / result artifacts, read or run as released, produce numbers different from the paper's reported values for the same experiment. HP-RESULT-ARTIFACT-MISMATCH (an implementation that computes a different loss/normalization/architecture than the equations state is HP-METHOD-DRIFT, not this)
10. Missing reproducibility artifacts — an empirical / agent / LLM paper ships neither code nor the prompts/configs/hyperparameters its results depend on, so the claim cannot be reproduced even in principle (the absence is L0-stated; what its results specifically need is L2-verified). HP-MISSING-REPRO-ARTIFACT

These are NOT inherently misconduct — they are failure modes of optimizing agents that lack an integrity constraint. This skill is that constraint, pointed outward, and it stays honest about what it can and cannot see.

Core principle (two independence axes)

The executor (Claude) collects paths + the ledger and passes them through; a fresh, different-family reviewer (codex) reads the code and proposes findings; a deterministic tool decides the verdict. Both axes from references/reviewer-independence.md hold:

• Layer 1 — cross-model (executor ≠ reviewer). The executor never summarizes, pre-judges, or leaks a hunch into the prompt — it ships only paths + claims.json
  • the checklist + the observability level. The reviewer is a different model family.
• Layer 2 — reviewer ≠ adjudicator. The reviewer is demoted from judge to evidence-extractor: it emits span-anchored findings; tools/adjudicate_findings.py computes overall_verdict by fixed rules. Same ledger + same findings → same verdict, with no model in the final decision.

How this differs from the other auditors (route correctly)

Auditor	Question it answers	Level
experiment-forensics (this)	Are the reported numbers what the eval code actually computes? (fake/derived GT, self-norm, phantom result, dead metric, verified scope, method drift, placeholder/fake data, code↔paper mismatch, missing repro artifacts)	L2 (L0/L1 → info-only; missing-repro absence is L0-stated, surfaced info here)
consistency-audit	Does the paper contradict ITSELF / does described method = evaluated method?	L0
baseline-comparison-audit	Are the right baselines present, tuned, and is "SOTA" earned?	L0 stated / L2 verified
citation-forensics	Do the cited papers exist and support the claim made?	L0
presentation-signals	Surface "AI-flavor" hints (auxiliary, capped at minor)	L0
adversarial-case-builder	Strongest evidence-bound rejection memo (no verdict weight)	any

Do NOT raise here (hand off instead): pure text-vs-text contradiction or scope-vs-evidence-in-text → consistency-audit; "first / SOTA / beats prior work" external truth → baseline-comparison-audit + citation-forensics (emit needs_external_check); citation existence/context → citation-forensics; surface/AI-flavor → presentation-signals; evaluation-design validity (train/test leakage, a conflicted/unvalidated LLM judge, declared-but-unreported conditions) → eval-design-forensics (family H, L0/L1 stated-tells — distinct from this skill's L2 code/result-integrity); the rejection memo → adversarial-case-builder. (Note: an LLM that produces the GROUND-TRUTH labels stays here as HP-FAKE-GT, L2 — only the LLM-as-judge validity question hands off to eval-design-forensics.)

Pipeline role + the anchoring model (read before running)

This is an auditor skill in the integrity-forensics pipeline (references/integrity-forensics-contract.md):

/evidence-ledger  →  claims.json (+ artifact_manifest.json, observability level L)
                          │
       experiment-forensics  ── reads the ledger, PROPOSES findings ──►  experiment-forensics.findings.json
                          │
   tools/adjudicate_findings.py  (deterministic; the ONLY thing that computes a verdict)

• It reads the ledger, it never re-reads the raw paper to invent structure, and it never computes the overall verdict — the orchestrator runs tools/adjudicate_findings.py for that. This skill stops at emitting findings.
• The anchor is always a PAPER claim. Every above-info finding cites a ledger claim_id and quotes a verbatim span of that claim's text_span (the paper number/scope/method sentence it undermines). The eval-code smoking gun (src/eval.py:88) is not a ledger claim, so it lives in the finding's description / recommended_reviewer_action, never as the anchor. No paper claim to anchor to ⇒ the finding cannot rise above info. (See worked examples.)
• Observability caps severity. Findings declare observability_level_required. Every code/result-level pattern is decidable only at L2; at L0/L1 it is emitted as info (Step 2). tools/adjudicate_findings.py is the structural backstop — any observability_level_required above the run's level is demoted to info.

Constants & Reviewer Calling Convention

• REVIEWER = mcp__codex__codex — model gpt-5.6-sol, config: {"model_reasoning_effort": "xhigh"}, sandbox: read-only, cwd = TARGET (the repo/paper dir, where the code + results live). A different model family from the executor (Claude). One fresh thread per audit pass; never mcp__codex__codex-reply across passes (the bias guard — reply is deliberately absent from allowed-tools). See references/reviewer-independence.md.
• PATTERNS_OWNED (must match references/hack-pattern-taxonomy.md, taxonomy_version 0.5): HP-FAKE-GT, HP-SELF-NORM, HP-PHANTOM-RESULT, HP-DEAD-METRIC, HP-SCOPE-INFLATE (verified form), HP-METHOD-DRIFT (L2 confirm), HP-SUSPICIOUS-REGULARITY (L2 confirm), HP-PLACEHOLDER-DATA (L2), HP-RESULT-ARTIFACT-MISMATCH (L2), HP-MISSING-REPRO-ARTIFACT (verdict-bearing at L2 — absence noticeable at L0/L1 but surfaced as info there, confirmed at L2).
• ROOT = $(git rev-parse --show-toplevel 2>/dev/null || pwd); L = observability_level from artifact_manifest.json; OUTPUT = experiment-forensics.findings.json (a bare JSON array).
• FILES (all under TARGET, next to claims.json): the only output is experiment-forensics.findings.json; the reviewer handoff is .aris/last_reviewer_response.txt; traces live in .aris/traces/experiment-forensics/<date>_run<NN>/.
• ⚠️ Shell state does not persist between Bash calls (cwd + env reset each call). Every block below re-resolves ROOT and TARGET at its top and reads L / paper_id from the manifest/ledger. Never rely on a variable from an earlier block, and never cd into TARGET (it would break ROOT resolution).

Division of labor (references/reviewer-independence.md):

• Executor (Claude) locates the ledger, lists artifact paths, gathers mechanical facts (file listings, literal-string greps, hashes), passes paths + the ledger + the checklist to the reviewer, validates the reviewer's spans, and writes the findings file. It never summarizes file contents, pre-judges, or leaks an opinion.
• Reviewer (codex / gpt-5.6-sol) reads ./claims.json and the code/result files directly from its cwd, proposes findings, and self-reports false_positive_risk. Told: the artifact paths, the ledger, the per-pass checklist, the level. Not told: any other auditor's findings, the executor's hunches, or "this looks AI-generated" — the tool audits integrity, not authorship.
• One fresh thread per pass. The A–F checklist is a single call; the optional G/H passes are each a NEW thread. If codex stalls (long sessions can hang), re-invoke the same prompt in a fresh thread — never codex-reply.

Step 1 — Preflight: resolve root, level, paper_id (self-contained)

ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
TARGET="$ARGUMENTS"          # paper-dir or repo-dir — ABSOLUTE path
case "$TARGET" in /*) ;; *) echo "FATAL: TARGET must be a non-empty ABSOLUTE path (got: '$TARGET'). Pass the paper/repo dir as an absolute path."; exit 1 ;; esac

# Toolchain must be reachable (invariant: ROOT = the Anti-Autoresearch checkout).
test -f "$ROOT/tools/build_manifest.py" || { echo "FATAL: Anti-Autoresearch tools not under $ROOT. Run this skill from inside the Anti-Autoresearch checkout (or point ROOT at it)."; exit 1; }

# The ledger is mandatory and is produced by /evidence-ledger. Do NOT invent it.
test -f "$TARGET/claims.json" || { echo "FATAL: $TARGET/claims.json missing. Run /evidence-ledger on $TARGET first (experiment-forensics reads the ledger)."; exit 1; }

# artifact_manifest.json derives the level. Build it with the real tool if absent
# (exact flags — confirm via: python3 "$ROOT/tools/build_manifest.py" --help).
if [ ! -f "$TARGET/artifact_manifest.json" ]; then
  PID=$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["paper_id"])' "$TARGET/claims.json")
  python3 "$ROOT/tools/build_manifest.py" --paper-id "$PID" --dir "$TARGET" --out "$TARGET/artifact_manifest.json"
fi

# Read the level (L) + paper_id. The manifest decides L; never override it.
python3 - "$TARGET" <<'PY'
import json, sys
t = sys.argv[1]
m = json.load(open(f"{t}/artifact_manifest.json")); c = json.load(open(f"{t}/claims.json"))
print(f"paper_id={c['paper_id']}  observability=L{int(m['observability_level'])}  "
      f"(rule: repo+results->L2, latex/no-results->L1, pdf/text-only->L0)")
PY
mkdir -p "$TARGET/.aris/traces/experiment-forensics"

Branch on L (read from the echo above; references/observability-levels.md): L < 2 → Step 2 (info-only) → Step 6. L == 2 (or 3 — treat as 2, never re-run code) → Steps 3–6.

Failure handling. No claims.json ⇒ stop (the FATAL above); the ledger has not been built and this skill never invents one — tell the user to run /evidence-ledger first. Never claim a higher level than the artifacts support: L is derived deterministically (repo + results → L2; latex, no results → L1; pdf/text only → L0), and the manifest decides it — you do not override it.

Step 2 — L0 / L1: info-only "could-not-verify" signals (the honesty backbone)

At L0/L1 there is no eval code and no result files, so you cannot decide any experiment-integrity pattern. Do not assert fraud, and do not duplicate the L0 text-scope check — consistency-audit owns scope from the manuscript. This skill's only job here is to mark, for the human, which numbers become checkable if a repo is released. Generate the signals deterministically from the ledger:

ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd); TARGET="$ARGUMENTS"
python3 - "$TARGET" <<'PY'
import json, re, sys
t = sys.argv[1]
claims = json.load(open(f"{t}/claims.json"))["claims"]
out, n = [], 1
def add(pat, title, desc, ev, act):
    global n
    out.append({"finding_id": f"EF{n:03d}", "skill": "experiment-forensics",
                "pattern_id": pat, "title": title, "description": desc,
                "severity": "info", "observability_level_required": 2,
                "evidence": ev, "verdict_local": "needs_external_check",
                "requires_external_check": True, "false_positive_risk": "high",
                "recommended_reviewer_action": act}); n += 1
def anc(c): return [{"claim_id": c["claim_id"], "span": c["text_span"], "location": c.get("location", {})}]

GT   = re.compile(r"reference|ground.?truth|\bgt\b|gold|agreement|target", re.I)
SCOPE= re.compile(r"comprehensive|extensive|robust|general|thorough|state[- ]of[- ]the[- ]art|\bSOTA\b", re.I)
nums = [c for c in claims if c.get("type") in ("number", "comparison")]

for c in nums:                                   # GT-provenance pointer
    if GT.search(c.get("text_span", "")):
        add("HP-FAKE-GT", "Ground-truth provenance not verifiable without the repo (L0 could-not-check)",
            "This number is reported against a 'reference/target/GT'. At this level it cannot be determined whether that reference is dataset-provided or derived from model outputs. NOT an allegation — verifiable only at L2 (eval code + result files).",
            anc(c), "Request the eval code + result files; verify GT provenance at L2 (HP-FAKE-GT).")
for c in nums:                                   # near-ceiling -> normalization pointer
    v = (c.get("value") or {}).get("normalized")
    if isinstance(v, (int, float)) and ((0.99 <= v <= 1.0) or (99.0 <= v <= 100.0)):
        add("HP-SELF-NORM", "Near-perfect score — normalization not verifiable from text",
            "A near-ceiling score with no raw value shown cannot be checked for self-normalization from a PDF.",
            anc(c), "At L2, check whether the metric is divided by the model's own output statistics (HP-SELF-NORM).")
for c in claims:                                 # verified-run-count pointer (defer text scope to consistency-audit)
    if SCOPE.search(c.get("text_span", "")):
        add("HP-SCOPE-INFLATE", "Scope language — actual run count not verifiable without the repo",
            "consistency-audit owns the L0 scope-vs-evidence-in-text check; experiment-forensics can only verify how many datasets/seeds/configs ACTUALLY ran at L2.",
            anc(c), "At L2, count the configs/seeds actually executed in the result files vs this wording.")
        break
add("HP-PHANTOM-RESULT", "Result existence not verifiable without backing files",
    "Whether each reported number maps to a real key in a real result file cannot be decided from a PDF.",
    (anc(nums[0]) if nums else []), "At L2, map each headline number to a result-file key (HP-PHANTOM-RESULT).")
add("HP-DEAD-METRIC", "Metric-code liveness not verifiable without the repo",
    "Whether any discussed metric is actually computed/called cannot be decided from a PDF.",
    [], "At L2, confirm each discussed metric is called and appears in a result file (HP-DEAD-METRIC).")
add("HP-PLACEHOLDER-DATA", "Placeholder / fake data in released code not verifiable without the repo",
    "Whether the released code still contains placeholder/dummy/fake data (e.g. a '# fake data for plotting' annotation or a hard-coded random array) feeding a reported figure/number cannot be decided from a PDF — flag the code marker, not who wrote it.",
    (anc(nums[0]) if nums else []), "At L2, grep the code for placeholder/dummy/fake markers and trace whether any reported figure/number is drawn from them (HP-PLACEHOLDER-DATA).")
add("HP-RESULT-ARTIFACT-MISMATCH", "Code-output vs paper-number agreement not verifiable without the repo",
    "Whether the released code / result artifacts actually produce the paper's reported numbers cannot be decided from a PDF. (A code-vs-equation implementation divergence is HP-METHOD-DRIFT, not this.)",
    (anc(nums[0]) if nums else []), "At L2, read the code's computation + result files and check each reported number against the code-produced value (HP-RESULT-ARTIFACT-MISMATCH).")
if nums:                                          # repro-artifact inventory pointer (absence is L0-observable)
    add("HP-MISSING-REPRO-ARTIFACT", "Reproducibility artifacts absent — empirical claims not checkable even in principle (absence noticeable at L0; verdict-bearing only at L2)",
        "This paper reports empirical/number results but the submission ships no eval code and no prompts/configs the results depend on. The ABSENCE is observable now (L0 'stated'); whether the SPECIFIC prompts/configs/hyperparameters its results need are present is verifiable only if a repo is released (L2). NOT a misconduct claim — a reproducibility gap. FP: a genuinely theoretical paper; double-blind submission norms (treat as a camera-ready expectation, lower severity).",
        anc(nums[0]), "Ask for the code + the exact prompts/configs/hyperparameters the reported numbers depend on; at L2 verify they are present and complete (HP-MISSING-REPRO-ARTIFACT).")

json.dump(out, open(f"{t}/experiment-forensics.findings.json", "w", encoding="utf-8"), indent=2, ensure_ascii=False)
print(f"L<2: wrote {len(out)} info 'could-not-verify' signals (severity=info, req:2 — the adjudicator keeps them at info).")
PY

Each emitted finding has the shape below (info-only, observability_level_required:2, empty evidence permitted only for info). Then go to Step 6 (no reviewer call needed at L<2 — these are deterministic pointers, not judgments):

{
  "finding_id": "EF001",
  "skill": "experiment-forensics",
  "pattern_id": "HP-FAKE-GT",
  "title": "Ground-truth provenance not verifiable without the repo (L0 could-not-check)",
  "description": "Claim C014 reports agreement against a 'reference'. At L0 (PDF only) it cannot be determined whether that reference is dataset-provided or derived from model outputs. This is NOT an allegation — verifiable only once the eval code + result files are available (L2).",
  "severity": "info",
  "observability_level_required": 2,
  "evidence": [
    {"claim_id": "C014", "span": "98% agreement with the reference",
     "location": {"file": "paper.txt", "section": "experiments"}}
  ],
  "verdict_local": "needs_external_check",
  "requires_external_check": true,
  "false_positive_risk": "high",
  "recommended_reviewer_action": "Request the evaluation code and result files; verify GT provenance at L2. Do not treat as a flag at this observability level."
}

Step 3 — L2: collect artifacts (executor — paths + mechanical FACTS only)

You (Claude) gather inputs and mechanical facts; you do not interpret, summarize, or pre-judge them (references/reviewer-independence.md). Listing what exists and grepping for a literal string are reproducible facts, not judgments — the same division citation-forensics uses for existence vs. context. Do not cd; use absolute $TARGET/... paths.

ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd); TARGET="$ARGUMENTS"
mkdir -p "$TARGET/.aris"

# (a) Eval / metric / test / score / benchmark / runner code + configs -> eval_paths.txt
find "$TARGET" -type f \( -name '*eval*.py' -o -name '*metric*.py' -o -name '*test*.py' \
   -o -name '*score*.py' -o -name '*benchmark*.py' -o -name 'run*.py' -o -name 'main.py' \
   -o -name '*.yaml' -o -name '*.toml' \) 2>/dev/null | sort > "$TARGET/.aris/eval_paths.txt"

# (b) Result files the paper's numbers should live in -> result_paths.txt
find "$TARGET/results" "$TARGET/outputs" "$TARGET/logs" -type f \( -name '*.json' -o -name '*.csv' \) 2>/dev/null | sort > "$TARGET/.aris/result_paths.txt"

# (c) Likely GT / reference loaders (FACT for the reviewer; do NOT judge) -> gt_grep.txt
grep -rInE 'ground.?truth|reference|target|label|gold|gt_|normaliz' --include='*.py' "$TARGET" 2>/dev/null | head -60 > "$TARGET/.aris/gt_grep.txt"

# (d) Phantom-result FACTS: grep EACH headline number from the ledger (no hardcoded token) -> number_grep.txt
python3 - "$TARGET" > "$TARGET/.aris/number_grep.txt" <<'PY'
import json, subprocess, sys
t = sys.argv[1]
claims = json.load(open(f"{t}/claims.json"))["claims"]
toks, seen = [], set()
for c in claims:
    if c.get("type") not in ("number", "comparison"): continue
    v = c.get("value") or {}
    for tok in (str(v.get("raw") or ""), ("" if v.get("normalized") is None else repr(v["normalized"]))):
        tok = tok.strip().rstrip("%").strip()
        if tok and tok not in seen:
            seen.add(tok); toks.append((c["claim_id"], tok))
for cid, tok in toks:
    hits = subprocess.run(["grep", "-rIn", "--", tok, f"{t}/results", f"{t}/outputs", f"{t}/logs"],
                          capture_output=True, text=True).stdout.strip()
    print(f"### {cid} token={tok!r}: {'FOUND' if hits else 'NOT FOUND under results/outputs/logs'}")
    if hits: print(hits)
PY

# (e) Reproducibility anchors: hash EVERY discovered eval script + result file -> hashes.txt
{ while IFS= read -r f; do [ -n "$f" ] && shasum -a 256 "$f"; done < "$TARGET/.aris/eval_paths.txt"
  while IFS= read -r f; do [ -n "$f" ] && shasum -a 256 "$f"; done < "$TARGET/.aris/result_paths.txt"; } 2>/dev/null > "$TARGET/.aris/hashes.txt"

# (f) Ledger claim subset the reviewer must anchor to -> claim_subset.json
python3 - "$TARGET" > "$TARGET/.aris/claim_subset.json" <<'PY'
import json, sys
t = sys.argv[1]
KEEP = {"number", "comparison", "scope", "method", "artifact_ref"}
claims = json.load(open(f"{t}/claims.json"))["claims"]
print(json.dumps([{"claim_id": c["claim_id"], "type": c["type"],
                   "text_span": c.get("text_span", ""), "location": c.get("location", {})}
                  for c in claims if c.get("type") in KEEP], indent=2, ensure_ascii=False))
PY

# (g) Placeholder / dummy / fake-data markers (FACT for the reviewer; do NOT judge) -> placeholder_grep.txt
grep -rInE 'placeholder|dummy|\bfake\b|fake data for plotting|mock(ed|_|\b)|TODO.*(replace|real|actual).*data|FIXME.*data|hard.?cod(e|ed)|np\.random\.|numpy\.random\.|random\.(rand|randn|randint|uniform|normal)|synthetic.*(demo|example|plot)' \
   --include='*.py' --include='*.ipynb' --include='*.r' --include='*.R' "$TARGET" 2>/dev/null | head -80 > "$TARGET/.aris/placeholder_grep.txt"

# (h) Repro-artifact inventory: do the runnable artifacts the results would need exist? (FACT; presence listing only) -> repro_inventory.txt
{ echo "## code (*.py):";        find "$TARGET" -type f -name '*.py' 2>/dev/null | head -20
  echo "## notebooks (*.ipynb):"; find "$TARGET" -type f -name '*.ipynb' 2>/dev/null | head -20
  echo "## prompts/templates:";   find "$TARGET" -type f \( -iname '*prompt*' -o -iname '*template*' -o -name '*.jinja' -o -name '*.j2' \) 2>/dev/null | head -40
  echo "## configs:";             find "$TARGET" -type f \( -name '*.yaml' -o -name '*.yml' -o -name '*.toml' -o -name '*.cfg' -o -name '*config*.json' -o -name '*.ini' \) 2>/dev/null | head -40
  echo "## env/deps:";            find "$TARGET" -type f \( -name 'requirements*.txt' -o -name 'environment*.yml' -o -name 'pyproject.toml' -o -name 'Pipfile' -o -name 'setup.py' -o -name '*.lock' \) 2>/dev/null | head -20
  echo "## run instructions:";    find "$TARGET" -type f \( -iname 'README*' -o -iname 'RUN*' -o -name 'Makefile' -o -name '*.sh' \) 2>/dev/null | head -20; } > "$TARGET/.aris/repro_inventory.txt"

# Machine validation gate: NO eval scripts AND NO result files => the manifest's L2 is
# unsupported -> re-derive the level (then run Step 2 if it drops below 2).
EV=$(grep -c . "$TARGET/.aris/eval_paths.txt"); RS=$(grep -c . "$TARGET/.aris/result_paths.txt")
echo "eval_scripts=$EV result_files=$RS  (paths + facts + claim_subset written under $TARGET/.aris/)"
if [ "$EV" -eq 0 ] && [ "$RS" -eq 0 ]; then
  echo "L2 UNSUPPORTED (no eval scripts, no result files) — re-deriving level:"
  python3 "$ROOT/tools/build_manifest.py" --paper-id "$(python3 -c 'import json,sys;print(json.load(open(sys.argv[1]))["paper_id"])' "$TARGET/claims.json")" --dir "$TARGET" --out "$TARGET/artifact_manifest.json"
fi

Re-read L (Step 1's reader); if it is now < 2, run Step 2 instead. The files written above (eval_paths.txt, result_paths.txt, gt_grep.txt, number_grep.txt, placeholder_grep.txt, repro_inventory.txt, hashes.txt, claim_subset.json, all under $TARGET/.aris/) are the reviewer's inputs — inline their literal contents into the Step 4 prompt. Do not summarize file CONTENTS — the reviewer reads the code/results directly via its cwd; you only pass paths, raw greps/hashes, and the claim subset.

Step 4 — L2: cross-model code audit (reviewer ≠ adjudicator)

Send paths + the ledger subset + the checklist to a fresh mcp__codex__codex. The reviewer reads the eval code line-by-line and proposes findings; it does not grade the paper. Call with: model: "gpt-5.6-sol", config: {"model_reasoning_effort": "xhigh"}, sandbox: "read-only", cwd: "<TARGET>" (so it can read ./claims.json, ./src/..., ./results/... directly), prompt: the block below. Assemble that prompt: by inlining the Step 3 .aris/ files at the [inline …] markers (read each, paste its literal contents — paths + raw facts + the claim subset, never a summary). One fresh thread; never codex-reply.

You are an experiment-integrity forensics reviewer. Your working directory is the
audited repo. Read the evidence ledger yourself at ./claims.json, then read EVERY
eval script line by line, plus the result files. Observability level = L2 (repo +
results present). For each check, PROPOSE findings — do NOT grade the paper, and
describe a DISCREPANCY to verify, never an accusation of misconduct.

Inputs (paths relative to your cwd; the executor inlines the Step 3 files verbatim):
- Ledger: ./claims.json   (anchor targets — use real claim_id + verbatim text_span)
- Ledger claim subset: [inline .aris/claim_subset.json]
- Eval / metric / test / runner scripts + configs: [inline .aris/eval_paths.txt]
- Result files: [inline .aris/result_paths.txt]
- Mechanical facts already gathered (raw, uninterpreted — GT greps, per-number result
  greps, placeholder/dummy/fake-marker greps, the repro-artifact inventory, file hashes):
  [inline .aris/gt_grep.txt + .aris/number_grep.txt + .aris/placeholder_grep.txt +
  .aris/repro_inventory.txt + .aris/hashes.txt]

## Checklist (map each finding to a pattern_id)
A. Ground-truth provenance  [HP-FAKE-GT, critical] — Where does "reference/target/
   GT/gold" come from in each eval? Loaded from the DATASET, or derived/generated
   from MODEL OUTPUTS and reported as performance? FP: explicitly labeled proxy;
   self-supervised by design.
B. Score normalization      [HP-SELF-NORM, critical] — Is any metric divided by
   max/min/mean of the model's OWN outputs to approach 1.0? Are raw scores shown?
   FP: standard min–max across ALL methods incl. baselines; raw+normalized both shown.
C. Result existence         [HP-PHANTOM-RESULT, critical] — Does each paper number
   map to a real key in a real result file, with a matching value? Any function
   referenced but never called? FP: file renamed/moved but present; number from a
   cited external reference.
D. Dead metric code         [HP-DEAD-METRIC, major] — A metric defined in eval code
   and DISCUSSED in the paper but never called / never in any result file. FP:
   utility kept for future use and not discussed as a result.
E. Scope (verified)         [HP-SCOPE-INFLATE, major] — How many datasets/seeds/
   configs ACTUALLY ran (count from result files/logs) vs the paper's scope
   language? FP: scope genuinely broad; qualifiers present.
F. Eval-type classification — For each eval, classify: real_gt | synthetic_proxy |
   self_supervised_proxy | simulation_only | human_eval. (A LABELED synthetic_proxy
   or self_supervised_proxy is legitimate — NOT HP-FAKE-GT.)

## Output (one JSON array, schemas/finding.schema.json) — output ONLY the array
For each finding:
{ "finding_id", "skill":"experiment-forensics", "pattern_id",
  "title", "description"  // name the exact file:line of the eval/result smoking gun,
  "severity", "observability_level_required": 2,
  "evidence": [ { "claim_id": <a ledger claim this undermines>,
                  "span": <VERBATIM substring of that claim's text_span>,
                  "location": {...} } ],
  "verdict_local": "fail|warn|clean|needs_external_check",
  "false_positive_risk": "low|medium|high",
  "alternative_explanation_checked": "<criticals ONLY — the benign readings you explicitly ruled out: rounding/display precision, unit or metric convention, statistical reporting convention, scope difference. Anchoring proves the text exists, NOT that your interpretation is right; a critical without this field demotes to major>",
  "recommended_reviewer_action": <what a human should ASK/CHECK> }

ANCHOR RULE: every finding above "info" MUST cite a ledger claim_id and quote a
verbatim span of THAT claim. The code path:line is forensic detail for the
description — it is NOT a valid anchor on its own. If no paper claim is undermined,
emit at most "info". Set false_positive_risk honestly. For "first/SOTA" claims you
cannot settle from the code, set verdict_local "needs_external_check". Do NOT output
an overall PASS/WARN/FAIL verdict — only the findings array. If nothing is wrong,
output [].

Additional focused passes (each a NEW fresh thread — never codex-reply), run only when the relevant ledger claims exist:

• G. Method identity [HP-METHOD-DRIFT, critical] — when a method claim exists (or consistency-audit raised an L0 suspicion): does the evaluated pipeline match the described method, or quietly run A-lite / A+oracle / extra data / a different backbone / test-time labels the method claims not to use — and does the code, as written, implement the described equations (same loss / normalization / architecture) rather than compute a different formula than the paper states? Both are method-identity drift (a code-vs-equation divergence belongs here, not in pass J). FP: a deliberately labeled ablation. Anchor to the method-description claim.
• H. Suspicious regularity [HP-SUSPICIOUS-REGULARITY, major] — when result tables show a too-clean arithmetic pattern (constant offset across rows, implausibly smooth monotonicity, identical decimals across unrelated settings): confirm against the actual result files/code. FP is high (deterministic metrics, integer scores, rounding, a real linear trend) — keep severity honest; never a "fabricated" grade.
• I. Placeholder / fake data [HP-PLACEHOLDER-DATA, critical] — when figure/number claims exist and the placeholder-marker grep (.aris/placeholder_grep.txt) is non-empty: does the released code still contain placeholder / dummy / fake data — stub annotations (# fake data for plotting, dummy, TODO: replace with real data), hard-coded arrays, or np.random.* feeding a plot (flag the marker, don't infer who wrote it) — and does a REPORTED figure/number derive from it rather than from a real run? Trace each flagged line to the figure/table it produces. FP: a clearly-labeled toy example or unit-test fixture that feeds NO reported result. Anchor to the figure/number claim the placeholder data produces.
• J. Result/artifact fidelity [HP-RESULT-ARTIFACT-MISMATCH, critical] — when number claims exist: does the code / result artifacts, run as written, actually produce the paper's reported numbers? Read the metric computation + the result files and compare each headline number to the code-produced value (.aris/number_grep.txt is the starting map). Strictly artifact-number vs paper-number. FP: seed / version / hardware variance within a stated tolerance; a documented post-hoc correction. Anchor to the number claim that diverges. An implementation that computes a different formula than the paper's equations is HP-METHOD-DRIFT (pass G), not this — route a code-vs-equation divergence there; THIS pass only asks whether the reported numbers reproduce.
• K. Reproducibility artifacts [HP-MISSING-REPRO-ARTIFACT, major] — when the paper is empirical / agent / LLM-driven (number or agent/LLM-pipeline claims exist): consult the repro-artifact inventory (.aris/repro_inventory.txt) and judge whether the prompts / configs / hyperparameters / seeds the REPORTED results depend on are actually present and complete enough to reproduce them — e.g. an agent/LLM paper that ships code but omits the prompt templates or the model/config settings its numbers depend on. FP: a genuinely theoretical paper (no empirical claim to reproduce); double-blind submission norms (treat as a camera-ready expectation → lower severity / needs_external_check). Anchor to the empirical claim whose artifacts are missing. (At L0/L1 the bare absence is already surfaced as an info pointer in Step 2; this pass is the L2 verification of what the results specifically need.)

Save the handoff + failure handling. Write the reviewer's full raw reply to "$TARGET/.aris/last_reviewer_response.txt" (use Write) before validating, and also preserve each call's prompt + reply as a per-call pair that never clobbers across batches: Write them to "$TARGET/.aris/traces/experiment-forensics/pending/prompt_<NN>.txt" and ".../response_<NN>.txt" (NN = 01, 02, … one per fresh thread; Step 6 moves pending/ into the dated run dir). If the codex call stalls or errors (long sessions can hang), re-invoke the same prompt in a fresh thread (never codex-reply); if it still fails, write [] to experiment-forensics.findings.json, record "experiment-forensics": "review_unavailable" in the run's coverage.json (when orchestrated by /anti-autoresearch), and proceed — never fabricate findings. If the repo is too large for one thread, split the result files into batches, run one fresh thread per batch (same prompt, different file lists), overwrite last_reviewer_response.txt with that batch's reply, and run Step 5 once per batch (the validator merges + dedupes into the output) — the per-call pending/ files keep every batch's raw trace.

Step 5 — Validate every reviewer finding (the executor gate)

The reviewer proposes; you verify before keeping. This mirrors the adjudicator's _anchored check exactly (span is a whitespace-normalized substring of the ledger claim, never the reverse), reads L from the manifest (not a persisted variable), extracts the JSON array robustly from the raw reply, re-gates any previously-saved findings together with the new ones, and merges + re-ids (so no stale above-info survives a multi-batch merge):

ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd); TARGET="$ARGUMENTS"
python3 - "$TARGET" "$TARGET/.aris/last_reviewer_response.txt" <<'PY'
import json, re, sys
t, resp = sys.argv[1], sys.argv[2]
OUT = f"{t}/experiment-forensics.findings.json"
L = int(json.load(open(f"{t}/artifact_manifest.json"))["observability_level"])
claims = {c["claim_id"]: c.get("text_span", "")
          for c in json.load(open(f"{t}/claims.json"))["claims"] if c.get("claim_id")}
OWNED = {"HP-FAKE-GT","HP-SELF-NORM","HP-PHANTOM-RESULT","HP-DEAD-METRIC",
         "HP-SCOPE-INFLATE","HP-METHOD-DRIFT","HP-SUSPICIOUS-REGULARITY",
         "HP-PLACEHOLDER-DATA","HP-RESULT-ARTIFACT-MISMATCH","HP-MISSING-REPRO-ARTIFACT"}
SEV = {"info":0,"minor":1,"major":2,"critical":3}
norm = lambda s: " ".join((s or "").split())

def extract(x):                       # robust: fenced block -> whole -> first [...]
    m = re.search(r"```(?:json)?\s*(\[.*\])\s*```", x, re.S)
    for s in ([m.group(1)] if m else []) + [x]:
        try: return json.loads(s)
        except Exception: pass
    i, j = x.find("["), x.rfind("]")
    if 0 <= i < j:
        try: return json.loads(x[i:j+1])
        except Exception: pass
    return None

try: raw = open(resp, encoding="utf-8").read()
except Exception: raw = ""
proposed = extract(raw)
if proposed is None:
    print("PARSE-FAIL: no JSON array in reviewer response; merging nothing new."); proposed = []
if isinstance(proposed, dict): proposed = proposed.get("findings", [])

try: base = json.load(open(OUT))
except Exception: base = []

gated = []                                                        # re-gate BASE + new together
for f in base + proposed:
    f["skill"] = "experiment-forensics"
    f.setdefault("title", f.get("pattern_id", "experiment-forensics finding"))
    f.setdefault("verdict_local", "needs_external_check")
    f.setdefault("observability_level_required", 2)
    f["evidence"] = [ev for ev in (f.get("evidence") or [])
                     if ev.get("claim_id") and (ev.get("span") or "").strip()]
    sev, notes = (f.get("severity", "info") if f.get("severity") in SEV else "info"), []
    if L < 2 and sev != "info":                                  # (1) L<2 => info-only
        sev = "info"; f["observability_level_required"] = 2; notes.append("L<2-info-only")
    if f.get("pattern_id") and f["pattern_id"] not in OWNED and sev != "info":  # (2) owned pattern
        sev = "info"; notes.append("pattern-not-owned")
    req = f.get("observability_level_required")                  # (3) observability gate
    if sev != "info" and not (type(req) is int and 0 <= req <= 3):
        sev = "info"; notes.append("undeclared-observability")
    elif sev != "info" and req > L:
        sev = "info"; notes.append(f"obs(req{req}>run{L})")
    if sev != "info":                                            # (4) ANCHOR gate
        ok = any(ev.get("claim_id") in claims and norm(ev.get("span"))
                 and norm(ev["span"]) in norm(claims[ev["claim_id"]])
                 for ev in f["evidence"])
        if not ok: sev = "info"; notes.append("unanchored")
    if sev in ("critical","major") and not any((ev.get("span") or "").strip() for ev in f["evidence"]):
        sev = "info"; notes.append("no-span")                    # (5) high sev needs a span
    f["severity"] = sev; f.setdefault("false_positive_risk", "medium")
    if notes: f["_executor_demotions"] = notes
    gated.append(f)

seen, merged = set(), []                                          # dedupe (base already re-gated)
for f in gated:
    e0 = (f.get("evidence") or [{}])[0]
    key = (f.get("pattern_id"), e0.get("claim_id"), e0.get("span"), f.get("title"))
    if key in seen: continue
    seen.add(key); merged.append(f)
for k, f in enumerate(merged, 1): f["finding_id"] = f"EF{k:03d}"  # re-id sequentially
json.dump(merged, open(OUT, "w", encoding="utf-8"), indent=2, ensure_ascii=False)
print(f"kept={len(merged)} above_info={sum(1 for x in merged if x['severity']!='info')} -> {OUT}")
PY

The executor never re-grades a finding's severity by hand (reviewer ≠ adjudicator: semantic FP calls belong to the reviewer, the verdict to tools/adjudicate_findings.py — the deterministic gates above are the only severity changes the executor makes). The FP cases below are already in the Step 4 checklist as the "FP:" clauses; the reviewer suppresses them at proposal time via false_positive_risk / verdict_local / info. If a proposed finding clearly matches one of them but the reviewer did not down-rank it, do not hand-edit the verdict — re-run Step 4 in a fresh thread so the reviewer re-judges:

• Eval-type ⇒ FP: a labeled synthetic_proxy / self_supervised_proxy is not HP-FAKE-GT.
• HP-SELF-NORM FP: standard min–max across all methods (incl. baselines), or raw+normalized both shown.
• HP-PHANTOM-RESULT FP: the file was renamed/moved but is present, or the number is a cited external reference.
• HP-SUSPICIOUS-REGULARITY: false_positive_risk: high by default; never a "fabricated" grade — a prompt to check.
• HP-PLACEHOLDER-DATA FP: a clearly-labeled toy example / unit-test fixture that feeds NO reported result is not a flag.
• HP-RESULT-ARTIFACT-MISMATCH FP: seed / version / hardware variance within a stated tolerance, or a documented post-hoc correction.
• HP-MISSING-REPRO-ARTIFACT FP: a genuinely theoretical paper (no empirical claim to reproduce), or double-blind submission norms (treat as a camera-ready expectation → lower severity / needs_external_check).

The only executor-side edits to experiment-forensics.findings.json are mechanical, non-semantic provenance stamps: set reviewer: {"model":"gpt-5.6-sol","reasoning":"xhigh", "thread_id":<id>,"deterministic":false} and, where useful, evidence[].artifact_hash = the ledger claim's evidence_anchor (the code-file sha goes in the description). Severity is never hand-edited up or down.

An empty array is a valid, honest output — if every proposed finding was demoted or none was proposed, experiment-forensics.findings.json may be [].

Worked L2 finding (the full, copyable shape):

{
  "finding_id": "EF002",
  "skill": "experiment-forensics",
  "pattern_id": "HP-SELF-NORM",
  "title": "Headline score is normalized by the model's own output maximum",
  "description": "The abstract headline metric (claim C014) is reported as 0.98. In the pipeline, results/main.json stores score_norm=0.98 computed at src/eval.py:88 (sha256 a1b2c3…) as raw_score / max(model_outputs) — the divisor is the model's OWN output max, not a fixed scale or a cross-method min–max. The raw score at the same key is 0.41, and no raw column appears in the paper. Discrepancy to verify: a metric approaching 1.0 via self-referential normalization is not comparable to baselines.",
  "severity": "critical",
  "observability_level_required": 2,
  "evidence": [
    {"claim_id": "C014", "span": "achieves a score of 0.98",
     "location": {"file": "main.tex", "section": "abstract"},
     "artifact_hash": "<sha256 of main.tex from the ledger's evidence_anchor>"}
  ],
  "verdict_local": "fail",
  "reviewer": {"model": "<resolved — what ACTUALLY ran (target: gpt-5.6-sol)>", "reasoning": "<resolved (target: xhigh)>", "thread_id": "<codex thread>", "deterministic": false},
  "false_positive_risk": "low",
  "requires_external_check": false,
  "recommended_reviewer_action": "Ask the authors for the raw (un-normalized) score and the exact normalization denominator; confirm the same normalization is applied identically to all baselines. If the divisor is the model's own output statistics, the 0.98 headline is not a valid cross-method comparison."
}

Step 6 — Emit, cross-reference, trace

The validated array is already written to $TARGET/experiment-forensics.findings.json (a bare JSON array conforming to schemas/finding.schema.json, re-id'd EF001…). No verdict here — the orchestrator runs the adjudicator next. Each code-level finding already names, via its claim_id, the paper claim it undermines, so the report can show "this number ↔ this code problem" — keep that linkage intact.

Trace every reviewer call (forensic — never silently dropped):

ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd); TARGET="$ARGUMENTS"
DATE=$(date +%Y-%m-%d); N=1
BASE="$TARGET/.aris/traces/experiment-forensics"
while [ -d "$BASE/${DATE}_run$(printf %02d $N)" ]; do N=$((N+1)); done
RUNDIR="$BASE/${DATE}_run$(printf %02d $N)"; mkdir -p "$RUNDIR"
# Move every per-call prompt_<NN>.txt / response_<NN>.txt pair (written in Step 4) into
# the dated run dir, so prompts + ALL batch replies are preserved (no clobber):
if [ -d "$BASE/pending" ]; then mv "$BASE/pending/"* "$RUNDIR/" 2>/dev/null; rmdir "$BASE/pending" 2>/dev/null; fi
# Fallback: also keep the last raw reply (covers a single-call run that skipped pending/):
cp "$TARGET/.aris/last_reviewer_response.txt" "$RUNDIR/reviewer_response.txt" 2>/dev/null
python3 -c 'import json,sys;print("observability_level", json.load(open(sys.argv[1]))["observability_level"])' "$TARGET/artifact_manifest.json" > "$RUNDIR/level.txt"
ls -1 "$RUNDIR"

Then print a one-screen summary (no verdict):

🔬 Experiment Forensics — L<L>  (paper_id=<id>)
  findings: <N> total, <M> above info
  <EF0xx> <severity> <pattern_id> — <one-line title>
  Output: $TARGET/experiment-forensics.findings.json  (proposals only)
  Verdict: NOT computed here — handed to tools/adjudicate_findings.py via the orchestrator.

For reference only — the orchestrator (not this skill) later runs (note --ledger is REQUIRED: it is what re-verifies each finding quotes a verbatim ledger span; without it every above-info finding fails closed to info):

# DO NOT run here — this is the orchestrator's job.
python3 "$ROOT/tools/adjudicate_findings.py" --findings "$TARGET"/*.findings.json \
    --ledger "$TARGET/claims.json" --paper-id <id> --observability-level <L> \
    --taxonomy-version 0.5 --out "$TARGET/report.json" --md "$TARGET/REPORT.md"

Output contract

• $TARGET/experiment-forensics.findings.json — array of finding objects (schemas/finding.schema.json). At L0/L1: info-only, observability_level_required:2, requires_external_check:true. At L2: span-anchored critical/major/minor/info with honest false_positive_risk. [] is a valid clean result. This skill writes no report.json / REPORT.md.
• $TARGET/.aris/traces/experiment-forensics/<date>_run<NN>/ — raw reviewer prompt(s) + response(s) + the level used.

Key rules

• L0/L1 ⇒ no fraud verdicts. Below L2 this skill emits only info-level "needs code" signals (observability_level_required:2); the validator and the adjudicator both pin them to info. This is the honesty backbone.
• The anchor is a PAPER claim. Every above-info finding quotes a verbatim span of a real ledger claim; the eval-code file:line is forensic detail in the description, never the anchor. Unanchored ⇒ info.
• Labeled proxies are legitimate. A labeled synthetic_proxy / self-supervised- by-design eval is not HP-FAKE-GT. Honor the eval-type classification.
• Executor collects paths + mechanical facts; the reviewer judges the code. No summaries, hunches, or "this is AI-generated" leak into the prompt (reviewer-independence.md).
• Reviewer ≠ adjudicator. This skill proposes findings; only tools/adjudicate_findings.py computes the verdict. Never emit a verdict here.
• Fresh thread per pass; never codex-reply (omitted from allowed-tools on purpose — the bias guard).
• Discrepancy, not accusation. description and recommended_reviewer_action ask a reviewer to check/ask, never "reject" or "the authors faked X".
• Not an AI-text classifier. "Results look too clean" is HP-SUSPICIOUS-REGULARITY — high-FP, only major when the result files contradict the table (L2); a bare "looks fake" from a table is deferred to consistency-audit. Never infer authorship or misconduct from surface impressions (those live, capped at minor, in presentation-signals).
• pattern_id ∈ taxonomy v0.4 only (PATTERNS_OWNED); the taxonomy is a post-hoc mapping layer, not the detector.
• Detect-only. Never edit the audited paper or repo; the only files this skill writes are its own findings.json and trace artifacts.

When NOT to use this skill

• As the verdict. It proposes findings; tools/adjudicate_findings.py renders CLEAN_GIVEN_EVIDENCE / SOFT_FLAGS / HARD_FLAGS. Do not read this skill's output as a ruling.
• At L0/L1 to assert fraud. With no repo you get info-level "could-not-verify" signals, full stop — never a fraud claim from a PDF.
• Without claims.json. No ledger ⇒ nothing can be anchored ⇒ everything fails closed to info. Run /evidence-ledger first; this skill never invents structure from the raw PDF.
• For text-only contradictions / scope-in-text → /consistency-audit; for baseline integrity or "SOTA / first" → /baseline-comparison-audit (+ /citation-forensics), handed off via needs_external_check.
• For authorship / "is this AI-written". Out of scope — surface "AI-flavor" lives in /presentation-signals (auxiliary, capped at minor); this repo is not an AI-text classifier.
• On a timer. Re-firing adds no signal — only a higher observability level does; schedule the wait for artifacts, then run once at the new level (see the cadence fence at the top).

Review tracing

After each mcp__codex__codex call, save the trace following ARIS Policy C (forensic; never silently skip): write the exact prompt, the raw response, and the observability level into $TARGET/.aris/traces/experiment-forensics/<date>_run<NN>/, as set up in Step 6. Traces are the audit trail for a reproducible, defensible report and the evidence that reviewer-independence held (the executor passed only paths + the ledger, not summaries).

Acknowledgements

Ports the A–F integrity checks from ARIS experiment-audit, motivated by community-reported issues (#57, #131) where executor agents fabricated ground truth and self-normalized scores. Reframed for third-party forensics: ledger-anchored findings, observability tiers, and reviewer ≠ adjudicator.