Workflow 1.75: stress-test claims (regardless of main-experiment verdict) by swapping method, dataset, and model, then judging whether each variant agrees with the main experiment. Three stages with two integrity gates: Stage 1 audits the main experiment's eval method for every target claim; Stage 2 runs swap variants only on the top-K admitted claims picked by importance (K = `MAX_VERIFY_CLAIMS`, default 1); Stage 3 judges (binary pass/fail per variant), audits the variants, and computes a per-claim `robustness = #pass / N_eligible ∈ [0,1]` that decides PASS / FAIL / INCONCLUSIVE / ZERO_ELIGIBLE_VARIANTS (threshold default 0.5 → at least half of eligible variants must pass; e.g. at N=3 this means ≥2 of 3). INCONCLUSIVE = main-experiment integrity broken (Phase 2 FAIL — variants never ran); ZERO_ELIGIBLE_VARIANTS = variants ran but all failed integrity at Phase 9; INTEGRITY_ONLY = Stage 1 audit passed but Stage 2 was intentionally skipped (either `SWAP_VARIANTS=false` global mode or per-claim `MAX_VERIFY_CLAI
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Workflow 1.75: stress-test claims (regardless of main-experiment verdict) by swapping method, dataset, and model, then judging whether each variant agrees with the main experiment. Three stages with two integrity gates: Stage 1 audits the main experiment's eval method for every target claim; Stage 2 runs swap variants only on the top-K admitted claims picked by importance (K = `MAX_VERIFY_CLAIMS`, default 1); Stage 3 judges (binary pass/fail per variant), audits the variants, and computes a per-claim `robustness = #pass / N_eligible ∈ [0,1]` that decides PASS / FAIL / INCONCLUSIVE / ZERO_ELIGIBLE_VARIANTS (threshold default 0.5 → at least half of eligible variants must pass; e.g. at N=3 this means ≥2 of 3). INCONCLUSIVE = main-experiment integrity broken (Phase 2 FAIL — variants never ran); ZERO_ELIGIBLE_VARIANTS = variants ran but all failed integrity at Phase 9; INTEGRITY_ONLY = Stage 1 audit passed but Stage 2 was intentionally skipped (either `SWAP_VARIANTS=false` global mode or per-claim `MAX_VERIFY_CLAIMS` cap — distinguished by `stage2_skip_reason`). All are valid on purpose: iteration fixes the main experiment for INCONCLUSIVE, only the variants for ZERO_ELIGIBLE_VARIANTS, and takes no action for INTEGRITY_ONLY. The goal is objective correctness, not maximizing PASS. No cross-claim aggregation. Use when user says "verify", "robustness check", "stress test claim".
Stress-test claims by swapping method, dataset, and model — applied uniformly to every claim regardless of whether the main experiment supported or rejected it. Each variant runs, gets judged against the main experiment's verdict on the frozen claim, and contributes to a per-claim score; the claim passes if (default 0.5).
robustness
robustness ≥ ROBUSTNESS_THRESHOLD
For: $ARGUMENTS
Overview
/auto-experiment produces per-run verdicts; /result-to-claim turns them into per-claim support. This skill asks a narrower but harder question: does the variant reach the same conclusion as the main experiment when we change the method, the dataset, or the model?
Per-variant we judge agreement as binary (pass / fail) — there is no middle "partial" tier; anything short of unambiguous agreement counts as fail. Per claim we aggregate as robustness = #pass / N_eligible, counted over the variants whose integrity check passed. The claim PASSes iff robustness ≥ ROBUSTNESS_THRESHOLD (default 0.5 — at least half of eligible variants must pass; e.g. at N=3 this means ≥2 of 3), otherwise FAILs — unless the eligible set is empty, in which case the claim is ZERO_ELIGIBLE_VARIANTS (a distinct terminal state, see below).
The pipeline runs in three stages, with two integrity gates:
Stage 1 — Setup & Gate (Phases 1–2). Parse arguments and identify target claims (no cap here — Stage 1 audits every target claim), then invoke two cross-model audits per claim on refine-logs/: /experiment-audit for evaluation methodology (fake GT, score normalization, phantom results, dead metric code, scope overclaim) and /mechanism-audit for mechanism-intervention rigor (steering coefficient sweep + reserved checks). The Phase 2 gate verdict for each claim is a single derived value combined = max_severity(exp.overall_verdict, mech.overall_verdict) with n/a (no mechanism intervention used) treated as pass. If combined = FAIL (i.e., at least one sub-audit returned FAIL → max_severity propagates FAIL), no PASS/FAIL on the claim is meaningful — the claim is marked INCONCLUSIVE here and Phase 3 step 0 short-circuits the rest of verify (Phases 3–10) to Phase 11 for that claim only. The inconclusive_reason names which sub-audit failed so iteration knows whether to fix evaluation, mechanism, or both.
Stage 2 — Run variants (Phases 3–7). Phase 3 step 0 picks the top-K admitted claims by importance (K = MAX_VERIFY_CLAIMS, default 1) — un-picked ADMITTED claims are marked INTEGRITY_ONLY with stage2_skip_reason: max_verify_claims_cap and skip to Phase 11. For each picked claim, pick alternatives, critique the plan, implement and (optionally) code-review each variant, deploy. Stage 2 ends when all variants have completed and raw metrics are on disk — no LLM judgment yet. Stage 2's exit gate lets you eyeball raw variant numbers before letting Stage 3 interpret them.
Stage 3 — Judge, Audit & Aggregate (Phases 8–11). For each variant of each picked claim, invoke /result-to-claim to judge whether its data supports the claim (Phase 8). Then run the variant-level integrity audit per claim (Phase 9) — symmetric to Phase 2, /experiment-audit + /mechanism-audit both run on the variant directory; each variant's integrity_status is the combined max_severity(exp, mech). Integrity-FAIL variants are excluded from both numerator AND denominator. Then compute robustness once on the post-audit eligible set and assign PASS / FAIL / ZERO_ELIGIBLE_VARIANTS (Phase 10). Finally write the report (Phase 11). If fewer than MIN_VARIANTS_FOR_VERDICT variants survive integrity (default MIN_VARIANTS_FOR_VERDICT=1, so only N_eligible = 0 triggers this), the claim is marked ZERO_ELIGIBLE_VARIANTS — variants did run, they just all failed integrity, so the iteration loop's instruction is "fix the variant evaluation, do not touch the main experiment."
Each claim ends in one of five states — PASS (main-experiment verdict is robust, in either direction — supported-and-stable OR rejected-and-stable), FAIL (main-experiment verdict is fragile under swaps), INCONCLUSIVE (main-experiment evaluation method or mechanism rigor is broken at Phase 2; variants never ran), ZERO_ELIGIBLE_VARIANTS (variants ran but every one failed Phase 9 integrity; no robustness verdict computable), or INTEGRITY_ONLY (Stage 1 audit passed but Stage 2 was intentionally skipped — either SWAP_VARIANTS=false global mode, or per-claim MAX_VERIFY_CLAIMS cap; the two are distinguished by the stage2_skip_reason field). The pipeline's goal is objective correctness: PASS and FAIL are both valid scientific outcomes; INCONCLUSIVE and ZERO_ELIGIBLE_VARIANTS each flag a different methodology break that iteration must fix on a different surface (main experiment vs. variants); INTEGRITY_ONLY names what was checked (integrity) and what was not (robustness) so iteration can no-op cleanly.
When SWAP_VARIANTS=false (audit-only mode), Phase 2 runs per-claim as usual; a new step 4 at the end of Phase 2 pre-writes each claim's ROBUSTNESS.md (INTEGRITY_ONLY with stage2_skip_reason: swap_variants_false when combined pass/warn, INCONCLUSIVE when combined fail — same semantics as the full-pipeline INCONCLUSIVE) and jumps to Phase 11. Phases 3–10 do not run. Only PASS / FAIL / ZERO_ELIGIBLE_VARIANTS are replaced by INTEGRITY_ONLY; INCONCLUSIVE is unchanged.
No cross-claim aggregation: each claim is reported independently.
DIMENSIONS = model — Which swap axes to run, and therefore how many variants per claim (exactly one swap per listed axis). List or comma-separated subset of {method, dataset, model}. Examples:
Override: — dimensions: method,dataset,model to broaden. No separate "effort" multiplier — to test more variants, broaden DIMENSIONS (already capped at 3 axes); deeper analyses like multi-seed runs or 2-factor cross-axis swaps are out of scope for verify and belong in /ablation-planner.
VARIANTS_PER_CLAIM — derived, equals len(DIMENSIONS). Not a separately tunable constant.
TARGET_CLAIMS = all — Claims to verify. Default all covers every claim in EXPERIMENT_PLAN.md regardless of main-experiment verdict — both supported claims (verify the support is robust across swaps) and not-supported claims (verify the rejection is robust). Other options: passed (only claims with main_experiment_verdict = supported from refine-logs/main-experiment-verdicts.json), failed (only claims with main_experiment_verdict = not-supported — useful when you want to verify just the negative findings), or a specific claim id (C1, C2, ...). Note passed ∪ failed = all with no overlap.
MAX_VERIFY_CLAIMS = 1 — Cap on how many claims proceed from Stage 1 (main-experiment integrity audit) into Stage 2 (swap variants). Stage 1 always audits every target claim — the cap only gates Stage 2 entry. If the Stage-1-admitted pool exceeds the cap, Phase 3 step 0 picks the top-K claims by importance judgment (reading each admitted claim's statement against upstream context like IDEA_REPORT.md / ## Rationale sections; row order is not a priority signal). Un-picked admitted claims are marked INTEGRITY_ONLY with stage2_skip_reason: max_verify_claims_cap; user can swap-test them later via /auto-verify <claim-id> — resume: true (Stage 1 audit is reused via RESUME). Default 1 → exactly one swap-tested claim per pass (with default DIMENSIONS=model: 1 × 1 = 1 variant run per pass); raise via — max-verify-claims: 3 to broaden coverage.
CODE_REVIEW = true — external LLM reviewer checks each variant's code before deployment. Set false to skip.
SANITY_FIRST = true — run the cheapest variant first (smallest split, fewest steps) before launching the rest.
AUTO_DEPLOY = true — deploy variants automatically after implementation + review. Set false to manually inspect.
MAX_PARALLEL_RUNS = 4 — concurrent variant runs.
GPU_ID = auto — GPU device(s) to use for variant runs. Options: auto (inherit from environment / let launcher decide), a single id (0), or a comma-separated list (4,5,6,7). When set to anything other than auto, the skill passes CUDA_VISIBLE_DEVICES=<GPU_ID> as the first positional arg to /run-experiment (sanity checks and reviewer-triggered re-runs same convention); the run-experiment skill exports it internally before launching the experiment subprocess. Do not treat the positional as a shell prefix — /run-experiment is a Skill, not a shell command. Override: — gpu-id: 4,5,6,7
ROBUSTNESS_THRESHOLD = 0.5 — Hyperparameter (configurable). A claim passes verify iff robustness ≥ ROBUSTNESS_THRESHOLD, where robustness = #pass / N_eligible over the variants that survived the integrity audit (Phase 9). Default 0.5 ≈ "at least half of eligible variants must pass". Discrete consequences at typical N_eligible:
N_eligible = 1 (default under DIMENSIONS=model) → robustness ∈ {0, 1}; only 1/1 = 1.0 satisfies the threshold (any single fail ⇒ claim FAIL)
N_eligible = 2 → need ≥1 of 2 variants to pass (1/2 = 0.5 satisfies ≥ 0.5)
N_eligible = 3 → need ≥2 of 3 variants to pass (2/3 ≈ 0.667 satisfies; 1/3 ≈ 0.333 does not)
Tighten with — robustness-threshold: 0.67 to require unanimity at N=3 (need 3/3, since 2/3 ≈ 0.667 < 0.67); loosen to — robustness-threshold: 0.33 to admit any single passing variant at N=3 (1/3 ≈ 0.333 ≥ 0.33). Note: N_eligible excludes variants whose integrity check returned FAIL from both the numerator and the denominator (an untrusted variant means "we don't know what this variant says", not "this variant disagrees"). If N_eligible < MIN_VARIANTS_FOR_VERDICT, no PASS/FAIL verdict is issued — the claim is marked ZERO_ELIGIBLE_VARIANTS instead (distinct from INCONCLUSIVE; see Phase 10's final-state table).
MIN_VARIANTS_FOR_VERDICT = 1 — Minimum number of integrity-clean variants required to issue a PASS/FAIL verdict on a claim. Default 1 means even a single eligible variant still produces a verdict rather than ZERO_ELIGIBLE_VARIANTS. Set to 2 or 3 for stricter projects where you want at least two independent axes to agree before issuing PASS/FAIL. ZERO_ELIGIBLE_VARIANTS is only triggered when N_eligible < MIN_VARIANTS_FOR_VERDICT (i.e., default: every variant failed integrity → N_eligible = 0). Override: — min-variants-for-verdict: 2.
REVIEWER_BACKEND = llm-chat — External LLM reviewer via llm-chat MCP (model defers to LLM_MODEL env). Always ask for strict, high-rigor feedback.
COMPACT = false — When true, write only verify/VERIFY_REPORT.md (skip per-claim ROBUSTNESS.md).
AUTO_PROCEED = true — If user doesn't respond at a checkpoint, proceed with best option.
RESUME = false — When true, each phase checks if its primary artifact already exists non-empty and skips itself if so (see "Resume protocol" below). Useful for picking up after a crash. Default false = every phase always runs from scratch and overwrites prior artifacts. Resume never deletes pre-existing files.
STOP_AFTER_STAGE = none — Halt after completing the named stage. Options: 1 (stop after Phase 2 — main-experiment audits done, before any GPU spent on variants), 2 (stop after Phase 7 — variants deployed and completed, raw metrics on disk, before any LLM judgment / integrity audit / aggregation), 3 or none (default — run all 11 phases through the report). Useful for human-in-the-loop inspection: run Stage 1 only → review main-experiment audits → commit GPU → re-invoke; or stop at Stage 2 to eyeball raw variant metrics before letting LLMs interpret them. Override: — stop-after-stage: 1.
SWAP_VARIANTS = true — Whether the swap-variant stress test (Stages 2–3) runs on top of Stage 1's main-experiment integrity audit. Default true = full 3-stage pipeline. Set false for audit-only mode: Phase 2 runs as usual and its new step 4 pre-writes each claim's ROBUSTNESS.md — INTEGRITY_ONLY with stage2_skip_reason: swap_variants_false when combined pass/warn, INCONCLUSIVE when combined fail (same semantics as the full-pipeline INCONCLUSIVE) — then jumps to Phase 11. Phases 3–10 do not run. Phase 11 renders an audit-only report shape with a swap_variants: false banner. Distinct from STOP_AFTER_STAGE=1: that flag is a human-in-the-loop halt that leaves no final report; SWAP_VARIANTS=false treats Stage 1 as the intended terminal deliverable and always writes the report. To upgrade later without re-auditing: /auto-verify <same-args> — swap-variants: true, resume: true (Phase 2 audits reused via RESUME; only Stages 2–3 execute). Override: — swap-variants: false.
Standalone overrides: /auto-verify C1 — robustness-threshold: 0.67, max-verify-claims: 5, dimensions: method,dataset, min-variants-for-verdict: 2, gpu-id: 4,5,6,7, stop-after-stage: 1. (To restrict to only main-experiment-supported claims, pass target-claims: passed; for only main-experiment-rejected ones, target-claims: failed; default all is usually what you want. stop-after-stage: 1 lets you inspect the per-claim main_experiment_audit/ results before committing GPU to Stages 2–3.)
Inputs
This skill expects one or more of:
refine-logs/EXPERIMENT_PLAN.md — claim map with C1, C2, ... and evidence requirements (the ## Claim-to-Milestone Map table)
refine-logs/EXPERIMENT_RESULTS.md — post-hoc per-claim conclusions written by /auto-experiment (canonical source for main-experiment verdicts; Phase 1 step 2(b) parses this into refine-logs/main-experiment-verdicts.json)
refine-logs/EXPERIMENT_TRACKER.md — milestone-level run status (M1a, M2b, ...); rows used by /experiment-audit and /mechanism-audit for per-claim scoping
refine-logs/FINAL_PROPOSAL.md — method description for implementation context
idea-stage/IDEA_REPORT.md or idea-stage/IDEA_CANDIDATES.md — prior research used to pick alternatives
refine-logs/main-experiment-verdicts.json (built lazily in Phase 1 step 2(b) if absent) — structured per-claim main-experiment verdict, read by Phases 1 / 8
EXPERIMENT_AUDIT.json + MECHANISM_AUDIT.json (both built by Phase 2 per claim; if pre-existing, their combined verdict — max_severity(exp, mech) — is reused as the per-claim integrity gate)
Resource-Fidelity Harness does NOT apply to verify.refine-logs/FINAL_PROPOSAL.md / EXPERIMENT_PLAN.md may carry a resource_fidelity: strict marker (written by /auto-claim for the reproduction combination — behavior-source:given + mechanism:given). That marker binds the main experiment (/auto-experiment) only — it forbids cost-driven downscaling of the main experiment runs. Verify's whole job is to deliberately swap the model / dataset / method (the DIMENSIONS axes) to test robustness, so verify ignores this marker: do not treat a model/dataset swap as a harness violation, and do not refuse a swap because the main experiment was marked strict. Pick alternatives exactly as normal regardless of the marker.
If none exist, ask the user which claim to verify and accept a free-form claim statement.
Workflow
Resume protocol (only when RESUME = true)
Skip entirely if RESUME = false (default). When true, each phase begins with a skip-if-present check:
Phase
Primary artifact (skip key)
Notes
1
always run (cheap argument parsing)
—
2
for each target Cx, ALL of verify/<claim_dir>/main_experiment_audit/{EXPERIMENT_AUDIT.md, EXPERIMENT_AUDIT.json, MECHANISM_AUDIT.md, MECHANISM_AUDIT.json} exist non-empty
Main-experiment integrity audit, per claim, two cross-model audits. Skip the /experiment-audit call for any Cx whose EXPERIMENT_AUDIT.{md,json} are present; skip the /mechanism-audit call independently when MECHANISM_AUDIT.{md,json} are present. Even on full skip, re-read both JSONs and recompute the combined verdict every invocation (cheap, deterministic) to decide whether Cx enters Stages 2–3 or is short-circuited to INCONCLUSIVE.
3
Step 0 always runs (re-reads verify/INTEGRITY_AUDIT.md to rebuild ADMITTED/REJECTED buckets, tops up FAIL claims' ROBUSTNESS.md stub, then re-uses verify/STAGE2_PICK.json if present-non-empty to skip the LLM importance-judgment call and rebuild the PICKED set). Step 1 (pick-alternatives) skips per claim if verify/<claim_dir>/PLAN.md already exists non-empty.
Re-reading from disk is the only safe re-entry mechanism after a STOP_AFTER_STAGE=1 halt — Phase 3 cannot trust in-memory state from Phase 2.
4
always re-runs (no clean per-claim skip artifact in the current design)
Phase 4 is a single reviewer LLM call per claim — cheap, not GPU-bound. Skipping it would require Phase 4 to leave a deterministic sentinel (e.g., a reviewed: true frontmatter field in PLAN.md); since it doesn't, RESUME re-pays the LLM cost. Acceptable in practice.
5
verify/<claim_dir>/variants/<variant-tag>/ has code committed
Per-variant: skip code generation for variants whose directory is already populated.
6
reviewer-approved marker per variant (e.g., .code_review_passed)
Per-variant.
7
per-variant run status — sanity row + done in tracker
Per-variant /result-to-claim judgment. Skip the call for any variant whose verdict.json is already on disk.
9
for each admitted claim Cx, ALL of verify/<claim_dir>/variant_audit/{EXPERIMENT_AUDIT.md, EXPERIMENT_AUDIT.json, MECHANISM_AUDIT.md, MECHANISM_AUDIT.json} exist non-empty AND every Cx variant has both integrity_status AND integrity_breakdown in its verdict.json
Variant-level integrity audit, per claim (symmetric to Phase 2, two cross-model audits). Skip the /experiment-audit call when its two artifacts are present; skip /mechanism-audit independently when its two artifacts are present. The main-experiment-integrity section of INTEGRITY_AUDIT.md is filled by Phase 2, not here.
10
verify/<claim_dir>/ROBUSTNESS.md exists non-empty with verdict field per claim
Per-claim robustness aggregation. Skip when ROBUSTNESS.md is fully populated. Caveat (COMPACT mode): when COMPACT = true, ROBUSTNESS.md is intentionally not written, so this skip key never matches and Phase 10 always re-runs on RESUME. That's acceptable because Phase 10 is pure arithmetic over already-on-disk verdict.json files (no LLM calls, no GPU).
11
verify/VERIFY_REPORT.md exists non-empty AND all per-claim ROBUSTNESS.md verdicts present AND INTEGRITY_AUDIT.md present (covering main experiment + variants)
Skip the final report if everything is already aggregated + audited.
Log every skip as [resume] phase <N> skipped — <reason>. Resume never deletes pre-existing files. To force a variant to re-run, delete its directory under verify/<claim_dir>/variants/.
Two-audit rows (Phases 2 & 9) — top-level predicate vs per-sub-audit independence. The "Primary artifact" column lists ALL of the four files for the full-skip case. That top-level ALL of {…} predicate decides whether the phase emits [resume] phase <N> skipped and does no work. The per-sub-audit clauses ("skip /experiment-audit … skip /mechanism-audit independently …") run regardless of the top-level predicate — they are the partial-state branch:
Both pairs present → full skip (top-level predicate true).
Only one pair present → phase runs, but skips that sub-audit's call; the other sub-audit runs to populate its missing files.
Neither pair present → phase runs both sub-audits.
The combined-verdict computation (Phase 2 step 2 / Phase 9 step 2) always reads whichever JSONs end up on disk after the phase finishes, applying the Defensive Read rules described under Phase 2 step 2.
Path expansion in resume checks.<claim_dir> is a glob, not a literal: when testing whether a per-claim artifact exists, expand with ls -d verify/<claim_id>_*/ 2>/dev/null (or compgen -G "verify/<claim_id>_*"), then test [ -s "$dir/ROBUSTNESS.md" ]. A naive [ -s "verify/<claim_id>/ROBUSTNESS.md" ] will never match — the on-disk directory is verify/<claim_id>_<short_claim>/ (see the <claim_dir> notation block at the end of this file). The same expansion applies to every <claim_dir> reference in this skill.
Phase 1: Parse Arguments and Identify Target Claims
Parse $ARGUMENTS:
Empty → TARGET_CLAIMS = all (default): verify every claim in EXPERIMENT_PLAN.md, regardless of whether the main experiment supported or rejected it
A claim id (e.g., C1, C2) → verify that one claim only
A free-form phrase → match against claim statements in EXPERIMENT_PLAN.md; if no match, ask user
— target-claims: passed → restrict to only main-experiment-supported claims (main_experiment_verdict = supported from main-experiment-verdicts.json)
— target-claims: failed → restrict to only main-experiment-rejected claims (main_experiment_verdict = not-supported)
— dimensions: ... override → parse the comma-separated axis list. Validate each entry is one of method, dataset, model; reject unknown axes. Empty list is illegal — fall back to default model. Store the resolved list as the effective DIMENSIONS.
— stop-after-stage: <1|2|3|none> override → parse the value. 1 halts after Phase 2; 2 halts after Phase 7; 3 and none (default) run all 11 phases. Other values reject with [verify] invalid stop-after-stage: <value> — expected 1, 2, 3, or none. Store as effective STOP_AFTER_STAGE.
Build the target-claim list:
(a) Read the plan. Open refine-logs/EXPERIMENT_PLAN.md and parse the ## Claim-to-Milestone Map table (columns: Claim | Tests | Required milestones | Pass criterion). Each row is one claim. The "Required milestones" column gives that claim's milestone IDs (e.g. M1, M2), which downstream phases use to scope per-claim audits via EXPERIMENT_TRACKER.md (whose rows are milestone sub-tasks like M1a, M2b, ...).
(b) Resolve per-claim main-experiment verdicts — refine-logs/main-experiment-verdicts.json is the single source of truth for every downstream phase. Build it lazily:
if refine-logs/main-experiment-verdicts.json exists:
load it. Treat as cache-miss and rebuild if any Cx is missing, or if EXPERIMENT_RESULTS.md is newer (an iteration back-edge may have moved a verdict).
else (cache-miss):
Read refine-logs/EXPERIMENT_RESULTS.md (canonical post-hoc verdict source — written by /auto-experiment's final summary).
Try to extract a per-claim verdict directly from its prose:
- regex-friendly forms:
* "C1 ... is supported" → supported
* "C3 ... is falsified | rejected | not supported" → not-supported
* "C2 ... is partially supported | mixed | inconclusive" → not-supported
(binary scheme: anything short of unambiguous support is treated as not-supported)
- target vocabulary: {supported, not-supported}
If extraction is ambiguous OR EXPERIMENT_RESULTS.md has no per-claim paragraphs,
invoke /result-to-claim once on the whole results file to translate prose → structure:
/result-to-claim "refine-logs/EXPERIMENT_RESULTS.md — extract per-claim verdicts only"
/result-to-claim returns pass | fail per claim; map pass → supported, fail → not-supported.
Persist the result as refine-logs/main-experiment-verdicts.json:
{
"C1": { "main_experiment_verdict": "supported", "main_experiment_metric": "...", "confidence": "high" },
"C2": { "main_experiment_verdict": "not-supported", "main_experiment_metric": "...", "confidence": "medium" },
...
}
This file is read by Phase 1 (target-claim filtering + per-claim record building) and Phase 8 (carrying main_experiment_verdict into every variant's verdict.json). No later phase re-reads it: Phase 11 picks up main_experiment_verdict from verdict.json (which got it from Phase 8, which got it from this file). Never re-parse EXPERIMENT_RESULTS.md ad-hoc later in the pipeline.
If EXPERIMENT_RESULTS.md itself is missing, abort with [verify] no refine-logs/EXPERIMENT_RESULTS.md — run /auto-experiment first. Do not invent verdicts.
(c) Build per-claim records. For each target claim, collect:
claim_id — e.g. C1
statement — full claim text from the plan
short_claim — a 4-word snake_case slug derived from the claim's section heading or statement in EXPERIMENT_PLAN.md, used to name the per-claim folder. Example: claim C1's heading "Silhouette Diagnostic & Polysemantic-by-Context Taxonomy" → polysemantic_by_context; C3's "Ablation-Rank Faithfulness" → ablation_rank_faithfulness. Stay descriptive over clever; see the <claim_dir> notation block at the end of this file for naming rules.
linked_milestones — list of milestone IDs from the plan's Claim-to-Milestone Map, e.g. [M1, M2]. Phase 2 uses this to scope /experiment-audit — claim Cx.
minimum_evidence — from the plan's pass criterion column
main_experiment_metric, main_experiment_verdict (supported | not-supported), confidence — read from refine-logs/main-experiment-verdicts.json[Cx]
main_experiment_verdict for each Cx is fixed at this point and propagates unchanged through Phases 2, 8, 9, 10 — no phase re-derives it.
Present the target-claim summary:
🎯 Stage 1 audit targets: [N] claim(s) (all target claims are audited; MAX_VERIFY_CLAIMS = [cap] gates Stage 2 entry only)
- C1: [one-line statement] — main-experiment verdict: supported / not-supported, confidence: high/medium
main experiment: [method]+[dataset]+[model] → [metric = value]
- C2: ...
Dimensions to test: [DIMENSIONS list]
Variants per picked claim: [len(DIMENSIONS)] (one swap per listed axis)
Stage 2 cap (top-K by importance): [MAX_VERIFY_CLAIMS]
Expected Stage 2 variant runs (upper bound): [min(cap, N_admitted) × len(DIMENSIONS)] (N_admitted resolved after Phase 2)
Estimated GPU-hours: [X]
Proceeding to Phase 2 audit.
VARIANTS_PER_CLAIM = len(DIMENSIONS) — no scaling, no multiplier.
If the resolved target-claim list is empty after Phase 1 (e.g., TARGET_CLAIMS = passed but no claim is supported; TARGET_CLAIMS = failed but every claim was supported; or EXPERIMENT_PLAN.md is missing / has zero claims) → stop and report [verify] no target claims — abort. Do not invent claims or auto-relax TARGET_CLAIMS to fill the list. Return this to the orchestrator as a Round-End Decision (ended-needs-decision (verify: no-target)) so it writes the decision record and the user chooses the next round (see auto/SKILL.md → "Round-End Decision").
Why this runs first. Verify's PASS/FAIL is meaningful only if both (a) the main experiment's evaluation method is itself trustworthy AND (b) the interpretability mechanism backing the claim (if any) was actually tuned. If the main experiment derives its "ground truth" from another model's output, normalizes scores by the model's own max, claims numbers from files that don't exist, or generalizes from a handful of cases — or if the steering coefficient was copied verbatim from another paper without a sweep — then both supported-and-stable and rejected-and-stable verdicts are built on sand. Catching this before spending GPU on swap variants saves compute and prevents polluting the record with a confidently wrong verdict in either direction.
This is the gate for Stage 1. Two cross-model audits run per claim: /experiment-audit for evaluation-methodology honesty, /mechanism-audit for mechanism-intervention rigor. Each claim's evidence is scoped via EXPERIMENT_PLAN.md's ## Claim-to-Milestone Map (which gives each claim its required milestones) crossed with EXPERIMENT_TRACKER.md (whose rows are the milestone sub-tasks like M1a, M2b, ...). A clean claim is not punished by another claim's dirty evidence. Each claim's combined gate verdict (PASS/WARN/FAIL) is decided independently from max_severity(exp.overall_verdict, mech.overall_verdict), with n/a treated as pass so non-mechanistic claims are not penalised by the mechanism audit. PASS/WARN claims advance to Stages 2–3 (Phases 3–10); FAIL claims become INCONCLUSIVE for this run and skip straight to Phase 11. The full workflow continues if at least one claim is admitted.
Cost note. Per-claim auditing recomputes shared-infrastructure checks once per claim and now runs two cross-model audits (/experiment-audit + /mechanism-audit) per Cx — roughly 2 × N_target LLM calls, where N_target = |target claims| (Stage 1 audits every target claim; MAX_VERIFY_CLAIMS does not shrink this set). For projects with N_target > 10 or unusually large eval scripts, this can dominate verify cost; in that regime, consider a project-wide pre-check (/experiment-audit "refine-logs/" — claim <any-Cx> plus /mechanism-audit "refine-logs/" — claim <any-Cx>) as a cheap fast-fail before per-claim audits. The default MAX_VERIFY_CLAIMS = 1 bites only at Stage 2 (variant GPU cost), not Stage 1.
1. For each target claim Cx, invoke /experiment-audit AND /mechanism-audit scoped to Cx
Two cross-model audits run per claim, in sequence, into the same per-claim main_experiment_audit folder. /experiment-audit covers evaluation-methodology honesty (A–F: GT, normalization, file existence, dead code, scope, eval type); /mechanism-audit covers mechanism-intervention rigor (A: steering coefficient sweep; B–F reserved). The two are independent and write to disjoint filenames.
For each Cx in the target-claim list (from Phase 1), construct the per-claim folder slug <claim_dir> = <claim_id>_<short_claim> (see the <claim_dir> notation block at the end of this file) and run:
verify/<claim_dir>/main_experiment_audit/MECHANISM_AUDIT.json — overall_verdict is Cx's mechanism-rigor verdict (may be n/a when Cx uses no mechanism intervention)
/experiment-audit's 6 checks cover failure modes that would invalidate Cx's main-experiment verdict in either direction (i.e., regardless of whether the main experiment said supported or not-supported). Checks A/B/D/F audit the shared infrastructure (eval scripts, configs, dataset) that every claim uses; Checks C/E are claim-scoped to Cx:
A. GT provenance — is "ground truth" loaded from the dataset, or derived from model output?
B. Score normalization — is any metric divided by the model's own max/mean?
C. Result file existence (claim-scoped) — do the numbers Cx cites in EXPERIMENT_RESULTS.md / EXPERIMENT_TRACKER.md exist at the claimed paths/keys?
D. Dead code — are evaluation functions defined but never actually called?
E. Scope (claim-scoped) — does Cx's wording ("comprehensive", "extensive") exceed Cx's actual number of scenes/seeds?
F. Evaluation type — real_gt | synthetic_proxy | ...?
/mechanism-audit's checks audit mechanism-intervention rigor for Cx:
A. Steering Coefficient Sweep — was the coefficient's unit stated (raw multiplier / activation-norm multiple / σ_proj all acceptable); logged alongside a capability/coherence metric; locked mid-plateau; controlled with random-direction baseline; sign pattern preserved for asymmetric protocols? And, only where the run fell short of its target criteria, was α swept across a wide geometric range (≥ 30× largest-to-smallest nonzero |α|, ≥ 5 points, α = 0 included) before the shortfall was reported as a negative result? A run that met its criteria at a small α is never asked to sweep wider — the smallest sufficient α is the correct one, and a larger α is not more rigorous. Returns n/a when Cx's experiment uses no additive intervention on internal representations.
B–F. Reserved — placeholders for future mechanism-rigor checks (direction-extraction quality, site / layer choice, n_effective sufficiency, probe-vs-causal disentanglement, intervention scope). Currently report not_implemented.
2. Combine per-claim verdicts and decide per-claim gates
Read both EXPERIMENT_AUDIT.json and MECHANISM_AUDIT.json for each Cx, then compute the combined per-claim integrity verdict using max_severity over the two overall_verdict fields.
Defensive read — when a sub-audit's JSON is missing (file does not exist, empty, or unparseable):
MECHANISM_AUDIT.json missing → treat sev_mech := 0 (n/a-equivalent). Log [main-experiment-audit] claim=<Cx> mech_audit missing — defaulting sev_mech=0 (n/a). Run /mechanism-audit to populate. Do NOT abort the gate; the claim is then judged purely on /experiment-audit's verdict. This is the expected path on legacy refine-logs/ trees from before mechanism-audit was deployed.
EXPERIMENT_AUDIT.json missing → symmetric: sev_exp := 0 and log the symmetric warning. This is rarer (Phase 2 invokes experiment-audit first), but a resume from a corrupted/hand-edited tree can hit it.
BOTH missing on a non-skip path → hard abort with [verify] no integrity audits for <Cx> — Phase 2 did not run cleanly. Do not silently default to pass.
severity(fail) = 3
severity(warn) = 2
severity(pass) = 1
severity(n/a) = 0 # n/a contributes no severity — a claim that uses
# no mechanism intervention is not penalised
inverse_severity = { 1: "pass", 2: "warn", 3: "fail" } # defined only on {1,2,3};
# the s == 0 branch below
# is the only path to "pass"
# via severity 0.
combined_verdict =
let sev_exp = severity(exp.overall_verdict) # 0 if exp JSON missing
let sev_mech = severity(mech.overall_verdict) # 0 if mech JSON missing
let s = max(sev_exp, sev_mech)
if s == 0 then "pass" # both n/a (or both missing) → pass
else inverse_severity(s) # 1→pass, 2→warn, 3→fail
Branch independently on combined_verdict for each Cx:
Combined verdict
Action for Cx
Reason
pass
Admit Cx to Phases 3–10 normally.
Cx's evaluation method is trustworthy AND the mechanism (if any) was tuned with the necessary controls.
warn
Admit Cx, but Cx's row in INTEGRITY_AUDIT.md / VERIFY_REPORT.md carries [MAIN-EXPERIMENT INTEGRITY: WARN] with the offending sub-audit name (e.g., [MAIN-EXPERIMENT INTEGRITY: WARN — mechanism]). The final Cx PASS/FAIL is still issued.
Soft issue in at least one sub-audit (scope language slightly overclaims; α at plateau edge; …) — verdict still informative.
fail
Mark Cx INCONCLUSIVE with a sub-audit-specific inconclusive_reason: main-experiment integrity broken (exp = fail, mech ≠ fail), main-experiment mechanism rigor broken (mech = fail, exp ≠ fail), or main-experiment integrity broken (experiment + mechanism) (both fail). Each reason links to the relevant *_AUDIT.md (see step 3 below for the canonical strings). Skip Phases 3–10 for Cx only. Other claims continue independently.
Cx's main experiment cannot be trusted in either direction; running swaps on top would just compute robustness around a broken anchor.
The all-FAIL short-circuit (when every Cx returns combined fail, skip Phases 3–10 and jump to Phase 11) is handled by Phase 3's Step 0 — see below. Phase 2 itself only writes per-claim verdicts; it does not branch the overall workflow.
3. Initialize INTEGRITY_AUDIT.md with per-claim main-experiment verdict rollup
Write verify/INTEGRITY_AUDIT.md with the per-claim verdict table only — this file is the index; per-claim A–F detail lives under each <claim_dir>/main_experiment_audit/EXPERIMENT_AUDIT.md and <claim_dir>/main_experiment_audit/MECHANISM_AUDIT.md. Do not duplicate the per-claim reports here.
# Integrity Audit
**Overall**: [PASS | WARN | FAIL] ← max severity across main experiment + variants
**Main-experiment integrity (Phase 2)**: [PASS | WARN | FAIL] ← max severity across main-experiment combined verdicts
**Variant integrity (Phase 9)**: [PASS | WARN | FAIL] ← filled by Phase 9
## Main-experiment integrity (Phase 2, per-claim)
| Claim | Exp. audit | Mech. audit | Combined | Gate decision | Detail |
|-------|------------|-------------|----------|---------------------------------|--------|
| C1 | PASS | PASS | PASS | continue | verify/C1_xxx/main_experiment_audit/ |
| C2 | PASS | WARN | WARN | continue-with-warn (mechanism) | verify/C2_yyy/main_experiment_audit/ |
| C3 | FAIL | N/A | FAIL | INCONCLUSIVE (experiment broken)| verify/C3_zzz/main_experiment_audit/ |
| C4 | PASS | FAIL | FAIL | INCONCLUSIVE (mechanism broken) | verify/C4_www/main_experiment_audit/ |
> Column glossary:
> - **Exp. audit** — `overall_verdict` of `/experiment-audit` on this claim.
> - **Mech. audit** — `overall_verdict` of `/mechanism-audit` on this claim. `N/A` when the claim uses no mechanism intervention.
> - **Combined** — `max_severity(exp, mech)` with `fail > warn > pass > n/a` and `n/a` treated as `pass`.
> - **Gate decision** — what Phase 2 does with this claim. The reason tag in parentheses points to which sub-audit drove a WARN/FAIL.
## Variant integrity (Phase 9)
[filled by Phase 9 — only for claims admitted by Phase 2]
For every Cx whose combined verdict is fail, also pre-write verify/<claim_dir>/ROBUSTNESS.md with verdict: INCONCLUSIVE and a sub-audit-specific inconclusive_reason:
both fail → inconclusive_reason: main-experiment integrity broken (experiment + mechanism) — see verify/<claim_dir>/main_experiment_audit/{EXPERIMENT,MECHANISM}_AUDIT.md
This keeps Phase 11's inputs consistent and tells the iteration loop exactly which sub-audit to fix.
If all claims fail, additionally emit a [skipped — all main-experiment audits FAIL] placeholder line under ## Variant integrity (Phase 9) so the orchestrator's resume check (in auto/SKILL.md) sees a non-empty variant section and doesn't wrongly re-execute Phase 2.
Check SWAP_VARIANTSbefore the Stage 1 exit gate. When false:
For each target claim Cx, pre-write verify/<claim_dir>/ROBUSTNESS.md (per Phase 10's audit-only shape — see Phase 10 below) with the appropriate verdict:
Cx's Phase 2 combined verdict is pass or warn → verdict: INTEGRITY_ONLY, stage2_skip_reason: swap_variants_false
Cx's Phase 2 combined verdict is fail → verdict: INCONCLUSIVE (identical to the full-pipeline stub written in step 3 above)
Overwrite verify/INTEGRITY_AUDIT.md's ## Variant integrity (Phase 9) section with the sentinel [skipped — SWAP_VARIANTS=false]. This is distinct from [skipped — all main-experiment audits FAIL]; the orchestrator's resume check (auto/SKILL.md) must treat both as legitimate "Phase 9 did not run" markers. Update the top-level Overall line to reflect only the main-experiment section.
Jump to Phase 11 (Write VERIFY_REPORT and Handoff) with all target claims — including pass/warn claims that would normally advance to Stages 2–3. Phases 3–10 are entirely skipped: alternative-picking, variant implementation / review / deploy, per-variant judgment, Phase 9 variant integrity audit, and Phase 10's robustness arithmetic all no-op. ROBUSTNESS.md for every claim is already on disk from step 1 above, so Phase 11 renders directly from it.
Do not honor STOP_AFTER_STAGE == 1 after step 4 — Phase 11 must still run so an audit-only VERIFY_REPORT.md lands on disk. STOP_AFTER_STAGE == 1 is a human-in-the-loop halt for inspection without a final report; SWAP_VARIANTS == false is a policy decision that Stage 1 IS the final deliverable. If a user genuinely wants both (audit and then halt before the report), they can pass both flags and this step's Phase-11 jump still runs — the audit-only report is cheap to produce and safer for downstream tooling than an empty report slot.
Stage 1 exit gate
If SWAP_VARIANTS == false, this exit gate is unreachable (step 4 above already jumped to Phase 11). Otherwise, if STOP_AFTER_STAGE == 1, halt here. Print:
Do not write VERIFY_REPORT.md at this point — it is Phase 11's product and requires Stages 2–3 outputs to be meaningful.
Phase 3: Pick Alternatives per Claim
Stages 2–3 entry — read Stage 1 output first. Phase 3 is the re-entry point after the Stage 1 gate, including the resume case where an earlier STOP_AFTER_STAGE=1 run produced Stage 1 artifacts and a new invocation is now continuing. Always rebuild the admitted-claim list from verify/INTEGRITY_AUDIT.md on disk — do not rely on in-memory state from Phase 2, because Phase 2 may not have run in this invocation.
Skip entirely when SWAP_VARIANTS == false. Phase 2 step 4 already jumped to Phase 11 with per-claim verdicts written. On a resume from a prior audit-only run, Phase 3 branches on the current invocation's SWAP_VARIANTS:
swap-variants: true, resume: true (upgrade path) → Phase 3 rebuilds ADMITTED from Phase 2's per-claim table normally; Stages 2–3 execute for the first time.
SWAP_VARIANTS=false still → Phase 3 is a no-op. Log [verify] SWAP_VARIANTS=false — Phases 3–10 skipped; audit-only ROBUSTNESS.md written by Phase 2 step 4.
Step 0: Read Stage 1 output and route by per-claim main-experiment verdict
Readverify/INTEGRITY_AUDIT.md. If it does not exist or contains no per-claim "Main-experiment integrity (Phase 2, per-claim)" table, abort with [verify] Stage 1 output missing at verify/INTEGRITY_AUDIT.md — re-run from Phase 2 (or invoke /auto-verify without — stop-after-stage). Stages 2–3 cannot start without the Stage 1 verdicts.
Bucket the target claims by parsing the per-claim verdict column (each row is one target Cx):
skip Phases 3–10 entirely for this Cx — already INCONCLUSIVE
already set by Phase 2
anything else
—
abort with [verify] unexpected main-experiment verdict for <Cx>: <value> — expected PASS/WARN/FAIL
—
Safety net for REJECTED claims. For each FAIL claim, confirm verify/<claim_dir>/ROBUSTNESS.md exists with verdict: INCONCLUSIVE and a sub-audit-specific inconclusive_reason pointing to whichever of EXPERIMENT_AUDIT.md / MECHANISM_AUDIT.md drove the FAIL (Phase 2 step 3 pre-writes this stub based on which sub-audit returned fail). If the file is missing — e.g., Phase 2 crashed mid-write before the stub was emitted, or this is a resume from a hand-edited tree — re-read both audit JSONs, recompute the combined verdict + reason, and write the stub here. Phase 11 reads ROBUSTNESS.md directly, so every claim must have one on disk before Phases 3–10 finish.
Short-circuit when ADMITTED is empty. If every target claim came out FAIL at Phase 2, print and jump straight to Phase 11:
[verify] Stage 1 admitted 0/<N> claims — all FAIL → INCONCLUSIVE.
Phases 3–10 skipped (variants would just compute robustness around a broken anchor).
→ Phase 11 (final report) only.
Pick the Stage-2 claim set (top-K by importance). Compute K = min(MAX_VERIFY_CLAIMS, |ADMITTED|). If RESUME=true and verify/STAGE2_PICK.json exists non-empty with a picked_claims field, load it and skip to step 8. Otherwise, decide the pick:
Single-claim mode (Phase 1 resolved TARGET_CLAIMS to one specific claim id): if that claim is in ADMITTED, PICKED = [Cx]; otherwise PICKED = [] (the claim is already INCONCLUSIVE).
K == |ADMITTED|: no selection needed — PICKED = ADMITTED (all admitted claims proceed).
K < |ADMITTED|: pick K claims by importance judgment. Read each ADMITTED claim's statement (from the per-claim records built in Phase 1) alongside any upstream context that is present: refine-logs/EXPERIMENT_PLAN.md's ## Rationale / ## Motivation / ## Contributions narrative sections (if any), idea-stage/IDEA_REPORT.md, refine-logs/FINAL_PROPOSAL.md, and the orchestrator's Claim Ledger (refine-logs/CLAIMS_LEDGER.md) if it exists with per-claim centrality / priority fields. Judge which K claims are the most scientifically central to the research direction. Row order in EXPERIMENT_PLAN.md is NOT a priority signal — do not use it as a proxy. For each picked claim, write a one-sentence rationale.
Pre-write INTEGRITY_ONLY stubs for un-picked ADMITTED claims. For each Cx in stage2_deferred = ADMITTED \ PICKED, write verify/<claim_dir>/ROBUSTNESS.md with verdict: INTEGRITY_ONLY and stage2_skip_reason: max_verify_claims_cap. Phase 11 will render these from disk; Phases 4–10 skip them.
From this point on, "for each target claim" in Phases 4–10 means for each claim in PICKED. REJECTED and un-picked ADMITTED claims are not touched again until Phase 11 reads their pre-written ROBUSTNESS.md.
Step 1: Pick alternatives for each picked claim
For each claim in PICKED, follow the protocol in ./pick-alternatives/SKILL.md (a sub-skill of this skill, not a top-level slash command). Read its SKILL.md and execute its steps inline, passing the claim id, claim statement, and the resolved DIMENSIONS from Phase 1 as inputs.
What the sub-skill does (full protocol in ./pick-alternatives/SKILL.md):
Reads idea-stage/IDEA_REPORT.md and any prior research-lit outputs for candidate methods/datasets/models
When the claim is about LLM internal mechanisms or LLM interpretability, and method is in DIMENSIONS, also reads skills/mechanism-skills/SKILL.md and the main experiment's family sub-skill to surface within-family method-swap candidates (e.g., swap probing/residual-stream-states for probing/sae-feature-activation-state; causal-attribution/ablation for causal-attribution/patching or causal-attribution/attribution-patching; vocabulary-projection/residual-stream-state for vocabulary-projection/attention-head-output). Hard constraint: the method swap MUST stay within the same mechanism family. Cross-family candidates (e.g., probing → causal attribution) are filtered out — they answer a different question (does the claim survive a totally different mechanism?) and belong in a separate sweep, not this verify pass.
If research coverage is thin for any of the three dimensions (fewer than 2 credible candidates), calls /research-lit "alternative [method|dataset|model] for claim: [statement]" — extra: semantic-scholar to fill the gap
Asks the external LLM reviewer to rank candidates by "strongest independent test of the claim" (not "closest to the main experiment")
Returns a structured variant list: exactly one swap per axis in DIMENSIONS (e.g., DIMENSIONS=method,dataset,model → one method swap + one dataset swap + one model swap; DIMENSIONS=method → method swap only)
Expected return per claim (written to verify/<claim_dir>/PLAN.md):
## Claim [C1]: [statement]
### Main experiment (from /auto-experiment)
- Method: [M0], Dataset: [D0], Model: [Mdl0] → [metric = value]
### Variants
| # | Dimension | Swap | Replaces | Justification | Source |
|---|-----------|------|----------|---------------|--------|
| 1 | method | [M1] | [M0] | [why this tests the claim] | [paper/idea ref] |
| 2 | dataset | [D1] | [D0] | [different distribution, same construct] | [paper/idea ref] |
| 3 | model | [Mdl1] | [Mdl0] | [different scale/family] | [paper/idea ref] |
### Success Criterion (per variant)
Claim is supported if [metric] on variant is within [delta] of the main experiment, in the same direction.
Phase 4: Reviewer Critique of the Verify Plan
Before writing any code, send the aggregated plan to the external reviewer for one critique pass. This catches "too easy" swaps (e.g., same dataset family, same model family) that would let the claim pass trivially.
mcp__llm-chat__chat:
prompt: |
You are a rigorous ML reviewer auditing a claim-verification plan.
Our goal is to stress-test the following claim(s) by swapping method, dataset, and model —
one swap per dimension, independently — and checking whether each variant reaches the
SAME conclusion as the main experiment on the frozen claim. (Both "all reach supported" and
"all reach not-supported" pass; only inconsistency fails.)
Claims under verification:
[paste each target claim's statement, main-experiment verdict (supported | not-supported), main-experiment setup, and chosen variants]
For each variant, answer:
1. Is this swap a GENUINE test of the claim, or a trivial re-run with cosmetic changes?
2. Does the swap control the right confound? (e.g., dataset swap should change distribution,
not just sample count)
3. Are there obvious stronger swaps we missed that the research evidence supports?
4. If the variant's conclusion diverges from the main experiment's on this swap, what would that
tell us — and is the divergence informative?
Then rank the full variant list by "how much a reviewer would trust the conclusion if it stayed consistent with the main experiment across this swap."
Flag any variant as REJECT if it is a trivial or biased test. Propose a replacement.
Be strict. Do not accept swaps that were picked for convenience.
Apply the reviewer's feedback:
Replace rejected variants with the reviewer's proposals
Re-rank the run order by the reviewer's trust ordering
If the reviewer rejects ≥ 50% of variants, loop back to Phase 3 for that claim (max 2 rounds)
If llm-chat MCP is unavailable, log [pending external review] on the plan and continue (graceful degradation)
Save the updated plan to verify/<claim_dir>/PLAN.md.
Phase 5: Implement Variants
For each variant (ordered by reviewer trust), produce a runnable artifact with minimum diff from the main experiment.
Start from the main experiment script/config used in experiment. Locate it via EXPERIMENT_TRACKER.md's reproduce command.
Apply only the swap:
Method swap → new module or loss; keep data pipeline and model backbone frozen. For mechanism/interpretability claims, take the swap template from the chosen within-family submethod's skills/mechanism-skills/<family>/<submethod>/scripts/ and references/ directories — adapt, do not copy verbatim.
Dataset swap → new dataset loader + splits; keep method and model frozen
Model swap → new checkpoint / architecture flag; keep method and dataset frozen
Hold all other hyperparameters fixed. If a hyperparameter is incompatible (e.g., batch size on a larger model), document the adjustment and ensure the main experiment is re-run with the same adjustment so the comparison stays fair.
Fix seeds. Use the main experiment's seed list (seeds: [42] by default; [42, 43, 44] if EXPERIMENT_PLAN.md specifies multi-seed).
Save per-variant artifacts under verify/<claim_dir>/variants/<variant-tag>/:
config.yaml — full config, diffed from the main experiment at top
run.sh — one-line reproduce command
DIFF.md — what changed vs the main experiment, one paragraph
mcp__llm-chat__chat:
prompt: |
Review this variant's code for correctness and fairness vs the main experiment.
## Claim being tested:
[statement]
## Main-experiment setup:
[method/dataset/model + metric]
## Variant diff (what changed):
[paste DIFF.md + the actual code diff]
Check:
1. Does the variant implement the intended swap, and ONLY that swap?
2. Are any hyperparameters silently different from the main experiment? List each and justify.
3. Does evaluation still use the dataset's actual ground truth labels (not another model's output)?
4. Is the metric computed the same way as the main experiment?
5. If a hyperparameter MUST change (e.g., batch size for a larger model), is the main experiment
also adjusted so the comparison is apples-to-apples?
6. Any leakage risk introduced by the swap (e.g., model trained on the swapped dataset)?
For each issue: CRITICAL / MAJOR / MINOR and the exact fix.
On results:
No CRITICAL → proceed
CRITICAL found → fix and re-submit (max 2 rounds)
llm-chat MCP unavailable → skip silently, proceed
Phase 7: Sanity Check, then Deploy
GPU pinning. If GPU_ID is not auto, pass CUDA_VISIBLE_DEVICES=<GPU_ID> as the first positional arg to every variant's /run-experiment invocation (sanity runs, the full sweep, and any re-runs triggered by reviewer feedback); the run-experiment skill exports it internally. Do not prepend it as a shell prefix. Record the effective CUDA_VISIBLE_DEVICES in each variant's run.sh so reproductions land on the same devices.
If SANITY_FIRST = true, run the cheapest variant on its smallest split first:
Verify training loop runs, metrics save, GPU memory stays in bounds. If sanity fails, follow the auto-debug protocol from experiment/SKILL.md Phase 3 (max 3 attempts; invoke /codex:rescue if available).
Then deploy the full variant sweep. Route by total variant count (same rule as /auto-experiment Phase 4.0):
total_variants ≤ 5 (small passes — e.g. 1 claim × 3 axes = 3, or 2 claims × 2 axes = 4): dispatch directly via /run-experiment, one call per variant, up to MAX_PARALLEL_RUNS concurrent. When GPU_ID lists multiple devices and MAX_PARALLEL_RUNS > 1, partition the devices across concurrent variants (e.g., GPU_ID=4,5,6,7 with 2 parallel runs → variant A on 4,5, variant B on 6,7); do not co-schedule two variants on the same device unless memory measurements confirm it fits.
total_variants ≥ 6 (large verify pass — e.g., an explicit — max-verify-claims: 3, dimensions: method,dataset,model combo yields 3 × 3 = 9 variants, and — max-verify-claims: 5, dimensions: method,dataset,model yields 15; the new defaults MAX_VERIFY_CLAIMS=1 × DIMENSIONS=model = 1 do NOT reach this branch): dispatch via /experiment-queue instead, with one phase per claim (so OOM retry, stale-screen cleanup, and wave-transition GPU gate are active across the variant grid). Build a queue manifest with each claim's variants as a phase (no depends_on: between claims — they are independent), max_parallel: from MAX_PARALLEL_RUNS, gpus: from GPU_ID, oom_retry: {delay: 120, max_attempts: 3}. After the batch completes, invoke /monitor-experiment per variant to finalize runs/<variant-id>/cost.json.
The routing decision and threshold mirror /auto-experiment Phase 4.0; verify uses a slightly lower threshold (6 instead of 10) because verify variants tend to share GPU memory profile within a claim (same method, different swap axis), making OOM cascades more likely than in experiment's milestone runs. The downstream Stage 3 logic (Phase 8 judgment → Phase 9 audit → Phase 10 aggregation) runs unchanged on top of either dispatch path.
🚦 Checkpoint (deploy gate): Skip entirely when AUTO_PROCEED = true OR AUTO_DEPLOY = true — proceed straight to deploy. Only when AUTO_PROCEED = false AND AUTO_DEPLOY = false, call AskUserQuestion and block indefinitely until the user answers (no timeout — waiting is the intended human-in-the-loop behavior):
🔧 Variants ready to deploy:
Claim C1: [1 variant, ~X GPU-hours]
Total: ~X GPU-hours on [N] GPUs
AskUserQuestion:
question: "Variants are ready. Proceed with the deploy plan above?"
header: "Deploy plan"
options: [approve (recommended), narrow scope, abort]
On answer:
approve → continue to the deploy step.
narrow scope → re-prompt for which claims/variants to keep (free-form), then re-open this gate with the trimmed plan.
abort → halt verify, return a partial-completion summary to the orchestrator.
Stage 2 exit gate
If STOP_AFTER_STAGE == 2, halt here after every dispatched variant has completed (run.log written, result.json non-empty). Variants are not yet judged — no /result-to-claim calls, no consistent_with_main_experiment, no robustness. Print:
🛑 Stage 2 complete — halted by STOP_AFTER_STAGE=2.
Variants deployed and completed for [N] picked claims. Raw artifacts at:
verify/<claim_dir>/variants/*/result.json (raw metrics)
verify/<claim_dir>/variants/*/run.log (run logs)
No LLM judgment, integrity audit, or robustness aggregation has been done yet.
This gate exists so you can eyeball raw variant numbers before letting Stage 3
interpret them. To continue with Stage 3:
/auto-verify <same-args> — stop-after-stage: none, resume: true
Do not write verdict.json, ROBUSTNESS.md, INTEGRITY_AUDIT.md's variant section, or VERIFY_REPORT.md at this point — they are Stage 3 products.
Phase 8: Per-Variant Claim Judgment
After each variant completes, invoke /result-to-claim scoped to that variant with its literal question (no main-experiment-comparison framing — that step is computed locally, see below):
/result-to-claim "variant [claim-id]/[variant-tag] — frozen claim: [statement] — does this variant's evidence support the claim?"
/result-to-claim returns its standard literal claim_supported: pass | fail — i.e., "does the data support the claim?" — exactly as in /auto-experiment usage. Binary scheme: anything short of unambiguous support (partial / mixed / narrow coverage / wrong magnitude) is fail. No semantic overload, no main-experiment mixed into the LLM's reasoning.
Then compute consistent_with_main_experiment deterministically (no LLM call) by projecting claim_supported through main_experiment_verdict. There are two projection cases — one per possible main-experiment verdict:
consistent_with_main_experiment =
if main_experiment_verdict == "supported": claim_supported # pass→pass, fail→fail
if main_experiment_verdict == "not-supported": flip(claim_supported) # pass→fail, fail→pass
flip(pass) = fail, flip(fail) = pass. This is the field that drives robustness: variant matches main experiment's conclusion ⇔ consistent_with_main_experiment = pass. Two patterns count as agreement:
Field semantics (both fields needed — do NOT collapse them):
main_experiment_verdict — main experiment's own conclusion on the frozen claim (supported | not-supported), read from refine-logs/main-experiment-verdicts.json (built once in Phase 1 step 2(b) from EXPERIMENT_RESULTS.md). Same value for every variant under the same Cx.
claim_supported — literal: does this variant's data support the claim? Returned by /result-to-claim as pass | fail, same semantics as anywhere else in the pipeline.
consistent_with_main_experiment — derived: variant's conclusion matches main experiment's conclusion (pass | fail). Deterministic function of (claim_supported, main_experiment_verdict), computed and written exactly once here in Phase 8. This is what feeds robustness in Phase 10; no downstream phase recomputes or re-checks it.
Phase-8-written vs Phase-9-appended fields. Phase 8 writes exactly the fields shown in the JSON above and stops. The integrity_status and integrity_breakdown fields are appended later by Phase 9 after the variant-level integrity audit runs (see Phase 9 → "Map per-variant audit verdict"). On a fresh Phase 8 run those fields are absent; that is correct, not a bug.
Phase 9: Per-Claim Variant Integrity Audit
Runs for each claim Cx in PICKED (i.e., admitted by Phase 2 AND selected by the Phase 3 step 0 Stage-2 pick). Skipped for any Cx whose Phase 2 combined verdict was fail (already INCONCLUSIVE) OR whose Phase 3 step 0 outcome was INTEGRITY_ONLY (variants never ran). If every target claim failed Phase 2, this phase is skipped entirely.
This is the variant-level half of integrity auditing (the main experiment half was handled in Phase 2 on refine-logs/). It catches the failure modes that the rest of the pipeline can't see — phantom results, fake GT, dead metric code, scope over-claim, AND mechanism failure modes such as un-tuned steering coefficients in the variants — before robustness is computed in Phase 10. The two halves are symmetric: same /experiment-audit + /mechanism-audit pair, same per-claim scoping via — claim Cx, output written into symmetric subdirs (main_experiment_audit/ for Phase 2, variant_audit/ for Phase 9).
Invocation
For each picked claim Cx, invoke both audits on Cx's variant directory, scoped to Cx:
verify/<claim_dir>/variant_audit/EXPERIMENT_AUDIT.md — full A–F findings on Cx's variants
verify/<claim_dir>/variant_audit/EXPERIMENT_AUDIT.json — overall_verdict is Cx's variant-level methodology-integrity verdict
verify/<claim_dir>/variant_audit/MECHANISM_AUDIT.md — mechanism-rigor findings on Cx's variants (e.g., did each variant re-sweep α on the swapped substrate?)
verify/<claim_dir>/variant_audit/MECHANISM_AUDIT.json — overall_verdict is Cx's variant-level mechanism-rigor verdict (may be n/a)
Map per-variant audit verdict into integrity_status
For each variant under Cx, derive a per-variant severity. The audit skills' contract is claim-level (overall_verdict is one value for all of <Cx>'s scope); a per-variant breakdown is an optional extension. Phase 9 supports both:
# Per-variant breakdown — if available in the audit JSON
if exp.checks.per_variant exists:
per_variant_exp = exp.checks.per_variant[<variant-tag>] (PASS / WARN / FAIL)
else:
# Fallback: every variant under Cx inherits the claim-level verdict
per_variant_exp = exp.overall_verdict
if mech.checks.per_variant exists:
per_variant_mech = mech.checks.per_variant[<variant-tag>] (PASS / WARN / FAIL / N/A)
else:
per_variant_mech = mech.overall_verdict
integrity_status = max_severity(per_variant_exp, per_variant_mech)
# n/a contributes 0; fail > warn > pass > n/a
Per-variant contract note. Neither audit skill emits checks.per_variant today — that field is reserved for a future extension where the reviewer is asked to break its verdict down by variant tag. Until then, the fallback applies: a single broken variant under Cx propagates the failure to every variant under Cx (conservative, never silently lets a broken variant through; possibly over-inclusive). Per-variant attribution will tighten this when the audit contract grows.
Write that single integrity_status field into the variant's verdict.json. Also write an integrity_breakdown companion field documenting the two sub-verdicts ({experiment: "...", mechanism: "..."}) so iteration knows which sub-audit to fix.
pass → variant is trusted; counts in both numerator and denominator of robustness in Phase 10
warn → variant counts normally but is tagged [INTEGRITY: WARN] in ROBUSTNESS.md / VERIFY_REPORT.md, with the offending sub-audit shown in parentheses ([INTEGRITY: WARN — mechanism])
fail → variant is excluded from BOTH numerator AND denominator in Phase 10, tagged [INTEGRITY: FAIL — <experiment|mechanism|both>], with the audit's specific reason logged. The symmetric exclusion is deliberate: a failed-integrity variant means "we don't know what this variant would have said", not "this variant disagrees". Counting it as 0 in the numerator while keeping it in the denominator would bias robustness toward FAIL, violating objective correctness — a broken variant must not push the verdict in any direction, only shrink the eligible set.
Log per variant (symmetric to Phase 2's [main-experiment-audit] block):
Skip this file when COMPACT = true; the same content lives in VERIFY_REPORT.md's integrity section.
Phase 10: Compute per-Claim Robustness Score + Final State
For each picked claim Cx, fold its post-audit variant verdicts into a single robustness label. Phase 9 has already settled every variant's integrity_status, and Phase 8 has already settled every variant's consistent_with_main_experiment, so Phase 10 is a pure-arithmetic single pass over those fields. Un-picked ADMITTED claims already had ROBUSTNESS.md written in Phase 3 step 0 with verdict: INTEGRITY_ONLY; Phase 10 does not touch them.
Compute robustness on the post-audit eligible set
Each variant's consistent_with_main_experiment (written by Phase 8 from claim_supported × main_experiment_verdict) contributes:
N_eligible = count(variants where integrity_status ∈ {pass, warn})
#pass = count(variants where consistent_with_main_experiment == "pass" AND integrity_status ∈ {pass, warn})
robustness = #pass / N_eligible
(integrity-FAIL variants excluded from BOTH numerator and denominator)
Assign final state
Under SWAP_VARIANTS = false this phase is unreachable — Phase 2 step 4 already jumped straight to Phase 11 with INTEGRITY_ONLY / INCONCLUSIVE verdicts pre-written. The arithmetic below runs only under the full-pipeline path. Branch on N_eligible:
N_eligible < MIN_VARIANTS_FOR_VERDICT (default 1 → triggers only when N_eligible = 0, i.e. every variant failed integrity) → ZERO_ELIGIBLE_VARIANTS (distinct from INCONCLUSIVE, which is reserved for Phase 2 main-experiment-integrity failures) with zero_eligible_reason: <N_eligible>/<N_run> survived — breakdown: <N_exp_fail> experiment-FAIL, <N_mech_fail> mechanism-FAIL, <N_both_fail> both-FAIL (counts derived by inspecting each failed variant's integrity_breakdown field — a variant is counted in <N_both_fail> iff BOTH experiment and mechanism are fail, otherwise it falls into whichever sub-audit was fail). No PASS/FAIL on zero signal. Iteration's job here is to fix the variant scripts (not the main experiment) and re-invoke /auto-verify <claim-id> — resume: true; the main experiment already passed Phase 2.
N_eligible ≥ MIN_VARIANTS_FOR_VERDICT:
robustness ≥ ROBUSTNESS_THRESHOLD → PASS (main experiment's verdict is robust across swaps).
robustness < ROBUSTNESS_THRESHOLD → FAIL (main experiment's verdict is fragile under swaps; send to iteration).
No cross-claim aggregation — each claim's state is reported independently.
Important — PASS does NOT mean "claim is true", FAIL does NOT mean "claim is false".
State
Meaning
Example
PASS
Main experiment's verdict is robust — variants reach the same conclusion. Direction = whatever the main experiment said.
Main experiment's verdict is fragile — variants disagree. Sent to iteration.
main experiment supported + variants split → support flips under swaps
INCONCLUSIVE
Phase 2 combined verdict failed — main-experiment evaluation methodology AND/OR mechanism rigor is broken; variants were never run because computing robustness around a broken anchor would be meaningless. Iteration instruction: fix the failing main-experiment sub-audit (experiment / mechanism / both); do not change the claim. Semantics are identical whether SWAP_VARIANTS=true or false.
main-experiment combined-FAIL at Phase 2
ZERO_ELIGIBLE_VARIANTS
Variants ran but every one failed Phase 9 combined integrity, so N_eligible = 0. The main experiment is fine (Phase 2 was PASS/WARN); the problem is purely on the variant side. Iteration instruction: fix the variant scripts that failed integrity and re-invoke /auto-verify <claim-id> — resume: true; do not touch the main experiment. Unreachable under SWAP_VARIANTS=false (Phase 9 never runs).
every variant FAILed at Phase 9 (combined experiment OR mechanism)
INTEGRITY_ONLY
SWAP_VARIANTS=false AND Phase 2 combined verdict is pass or warn. Main experiment's evaluation methodology (and mechanism rigor, if any) is validated, but the swap stress test was intentionally skipped by policy. No robustness verdict; no PASS/FAIL. Iteration instruction: no back-edge action — record in Open Items with the suggestion to upgrade to full verify (/auto-verify — swap-variants: true, resume: true) when GPU budget allows. Never appears under the default SWAP_VARIANTS=true.
Stage 1 audit passed, Stage 2/3 skipped by policy flag
Write the final per-claim summary (skip if COMPACT = true, except for the audit-only shape below which is always written — see note at the end of this subsection):
## C1: robustness = [0.67] (threshold = [0.5], eligible = [3/3]) → [✅ PASS | ❌ FAIL | 🟡 INCONCLUSIVE | 🟠 ZERO_ELIGIBLE_VARIANTS]
- swap_variants_run: true
- Main-experiment verdict on C1: [supported | not-supported]
- Variant counts (over `consistent_with_main_experiment`): [N_pass] pass, [N_fail] fail
(of [N_eligible] eligible; [N_excluded] excluded for integrity reasons)
- Method dimension: matches the main experiment (consistent=pass, claim_supported=pass, delta +1.2%)
- Dataset dimension: matches the main experiment (consistent=pass, claim_supported=pass, delta -2.1%)
- Model dimension: diverges from the main experiment (consistent=fail, claim_supported=fail while main experiment=supported, delta -8.4% — conclusion flips under model-scale change)
Diese SKILL.md ist sehr gross, daher zeigt SkillsMP hier nur den ersten Abschnitt.Auf GitHub ansehen
