bugfix — Per-Task Aggregation

Generated: 2026-05-19T04:05:40Z

Inputs and source artifacts

Everything fed into this aggregation is committed; no private state.

• Trial input (task PRD). The exact prompt every tool saw for this task: _blind-eval/prd.md (omitted from public release).
• Per-tool prompt prefix. The tool-specific slash command bound to that PRD lives in scripts/manual-bench.sh.
• Judge input (verbatim request payload). What each of the 5 judges received per label per round — same blinded diff + rubric, varying only by model: _blind-eval/Alpha/round1/ (omitted from public release) (<judge>-judge.json.request.json).
• Judge prompt template. scripts/generate-judge-prompt-combined-v2.sh (omitted from public release).
• Methodology and threats to validity. PAPER.md (§1 methodology, §4 limitations) · README.md · landing page.

Methodology

• Tools under test: 8
• Blind labels: 40
• Layout: 3 rounds per (artifact, judge) — the canonical run (judge files flat at the label root) plus round1/ and round2/ rerun directories. The aggregator reads the label root plus ^round[0-9]+$ subdirs in union; pilot/sample dirs (e.g. roundcotsample*) are excluded.
• Judges: opus, grok420, glm51, GPT-5.4, mimo25pro (5-judge panel; each artifact scored 3 times — once per round — by every judge)
• Rubric: 20 items × 0–10 pts = 200 pt max
• Canonical score per judge file: sum(scores.values()) (not the stored total field)
• Reported tool mean: weighted mean of per-judge means (weights: opus×3, GPT-5.4×2, grok420×1, glm51×1, mimo25pro×1)
• Total judgments aggregated: 600

Caveats / threats to validity

• Judge weights are pre-registered, not derived. The 3 / 2 / 1 / 1 / 1 weighting is stored as judges.*.weight in versions.lock.json (committed 2026-05-12) and reflects the operator’s prior trust in the Anthropic (opus) and OpenAI (GPT-5.4) reviewers. An equal-weight aggregation is emitted alongside this report as final-report.equal-weight.md; the in-report Pooled Mean column is also the equal-weight comparator and lets readers verify rank-stability without leaving this file.
• Judge scorer asymmetries. GPT-5.4 is consistently the harshest scorer in the panel (lowest mean across labels). mimo25pro is the most lenient and occasionally emits 200/200 saturations; its weight of 1 dilutes the impact, but right-tail scores should be read in that context.
• σ decomposition. The per-tool standard deviation column is split into within_σ (within-judge spread — mean of the per-judge stdev across the 15 samples per (tool, judge): 5 trials × 3 rounds) and between_σ (judge base-rate spread — stdev of per-judge means). within_σ now bundles trial-to-trial output variance with round-to-round judge-sampler variance; the latter is small where temperature=0 is honored (OpenRouter, OpenCode Go) and absorbed in within_σ where it is not (Claude CLI, OpenAI /v1/responses). Within > between would indicate the tool’s output (combined with sampler drift) is unstable; the reverse means most variance is judge base-rate disagreement.
• Judge sampling not pinned. Temperature is fixed to 0 where the provider exposes it (OpenRouter, OpenCode Go). Claude CLI and OpenAI /v1/responses do not expose temperature/seed, so residual sampler variance is absorbed in per-judge σ rather than eliminated.
• R1 mechanical-fact override. Rubric items with deterministic answers (e.g. tsc_errors == 0) are rewritten post-hoc from auto-metrics.json to remove LLM arithmetic / classification drift. Items locked per task: feature 12/13/16/20, bugfix 14/15, refactor 13/14. Pre-override scores are preserved under scores_pre_r1 on every judged file (scripts/aggregate-results.sh runs an idempotent R1 sweep before aggregating).
• Blind eval is structural, not semantic. Tool identity is hidden via NATO labels and a path-/content-level scrub of tool-specific directories (.omc/, _bmad/, _bmad-output/, _bmad-core/, docs/bmad/, docs/superpowers/, plans/, .claudekit/, .gstack/, .superpowers/, .compound-engineering/, .ecc/, CLAUDE.md.original). auto-metrics.json is anonymised by stripping plugin_versions and collected_at. A skilled judge could still infer identity from idiosyncratic code style; we don’t claim semantic anonymity.
• Cohort span: 149.7h (2026-05-10 → 2026-05-16). Spans >24h indicate the cohort did not complete within a single day; scripts/audit-cohort-symmetry.py flags this as a soft warning. The longest spans in this report stem from the leak-fix re-judge pass (see docs/RERUN-PRE-PUBLISH.md).

Aggregate Scores per Tool

Column glossary — read this first. One row per tool; the columns are:

• Tool — the setup under test. Eight rows: bmad, claudekit, compound, ecc, gstack, omc, pure (no-addon baseline), superpower.
• Weighted Mean (bold; canonical rank column) — weighted average over judges: (3·opus + 2·GPT-5.4 + grok420 + glm51 + mimo25pro) / 8. Weights pre-registered in versions.lock.json and reflect operator trust in the Anthropic / OpenAI judges.
• Pooled Mean — straight equal-weight average over all 75 judgments (every judge counts the same, 1×). Quick sensitivity check: if Weighted and Pooled order the top tools the same way, the ranking is robust to the weighting scheme. The dedicated final-report.equal-weight.md is the full equal-weight comparator.
• Pooled σ — overall standard deviation across all 75 judgments (raw score spread before splitting variance sources).
• within_σ — within-judge spread. For each judge, compute σ across its 15 samples per tool (5 trials × 3 rounds), then average across the 5 judges. High within_σ means the same judge gave the tool different scores across runs — either the tool’s output varies trial-to-trial or judge sampler drift (where temperature=0 isn’t honored).
• between_σ — between-judge spread. Compute each judge’s mean for this tool, then take σ across those 5 per-judge means. High between_σ means judges systematically disagree (lenience drift). within_σ < between_σ is the healthy case: most of the noise is judge base-rate, not tool flakiness.
• N — total judgments aggregated for this tool. Should equal 75 when complete (5 trials × 5 judges × 3 rounds).
• n(opus) … n(mimo25pro) — how many of those judgments came from each judge. In a complete cohort each equals 15 (5 trials × 3 rounds); a lower value exposes a missing or in-progress backfill for that judge (not silently averaged away — the weighted mean drops absent slots from both numerator and denominator).

Inter-rater agreement (Krippendorff α)

α = 0.284 (interval level, judges as coders, blind labels as units, N=40 labels × 5 judges = 200 observations).

Krippendorff α measures how much the 5 judges agree on the absolute score for the same artifact. Conventional thresholds (Krippendorff 2011): α ≥ 0.800 supports firm conclusions; ≥ 0.667 supports tentative ones; < 0.667 is unreliable for absolute claims. Caveat: α punishes per-judge lenience drift hard — GPT-5.4 (panel-low) and mimo25pro (panel-high) are far apart on most artifacts even when they order tools the same way. The benchmark’s weighted-mean aggregation is less sensitive to any single judge’s base rate, but it does not make raw scores robust to per-judge lenience drift — the per-judge z-normalized table below is the actual mitigation for that; α surfaces the drift as a separate honesty metric.

Upper-bound caveat: α is computed on each (label, judge)'s mean across rounds, so round-to-round judge-sampler noise is averaged out before the reliability calculation. The reported α therefore overstates raw round-level inter-judge agreement — true per-round α is lower than the values shown here. Read these as a generous ceiling, not a point estimate.

Power analysis & detection threshold (MDE)

MDE ≈ 22.17 pts at α=0.05 (two-sided), power=0.80, n=5 trials per arm, σ_pool=11.14 pts (pooled across 8 tools using trial-level weighted means).

Two tool means whose gap is below MDE cannot be statistically distinguished at the standard α=0.05 / 80%-power threshold. The current cohort uses n=5 trials per cell, which is the binding constraint — judgments within a cell are correlated (same judge across rounds, same trial across rounds), so trials are the real degree of freedom.

• Rank-1 lead: 6.62 pts → below MDE — read as a tie
• Gaps rank-1 vs each lower tool (✓ = exceeds MDE, ⚠ = below MDE):
  • claudekit − ecc: 6.62 pts ⚠
  • claudekit − pure: 9.40 pts ⚠
  • claudekit − superpower: 12.53 pts ⚠
  • claudekit − compound: 12.68 pts ⚠
  • claudekit − bmad: 13.22 pts ⚠
  • claudekit − omc: 14.13 pts ⚠
  • claudekit − gstack: 18.97 pts ⚠

Implication for this cohort: at n=5 trials per cell, every per-task rank-1 lead falls below MDE (per-task MDEs and σ_pool for all three tasks are in results/power-analysis.json) — the top cluster is a statistical tie, not a ranking. The α/2 critical value is the exact Student-t quantile for df=2(n-1)=8 (≈2.306), not the normal z=1.96 — at n=5 this enlarges every MDE by ~12% (feature ≈19.33, bugfix ≈22.17, refactor ≈44.02). Under the corrected threshold the only gap that clears its task MDE anywhere in the corpus is ecc−gstack on feature (≈21.3 vs the 19.33 feature MDE); the previously-cited ecc−claudekit (≈18.3) and ecc−compound (≈18.6) feature gaps fall below MDE under the exact-t critical and are no longer treated as separations. No rank-1 lead on any task clears MDE; no gap on bugfix or refactor clears its own task MDE. Trial-to-trial variance (not judge noise) is the binding constraint: the n=3→n=5 expansion raised σ_pool on every task, so MDE did not follow the expected 1/√n drop (refactor worsened sharply, driven by gstack’s trial-4 refactor diff scoring ≈36/200 against ~178 on its other four). No family-wise correction is applied to the ≥21 pairwise gap tests — they are descriptive detection-threshold comparisons, not confirmatory hypothesis tests. This is exactly why post-hoc selective reruns are pre-registered as invalid. See docs/IMPROVEMENT-PLAN-NEXT-COHORT.md.

Outlier audit & rerun verdict

Round-level outlier check per the pre-registered rerun protocol (CLAUDE.md § Rerun): a round-judgment flags when |score − median(other rounds)| > 15 pts AND > 1.41 × spread(other rounds) (≈ 2σ on the 2 remaining samples).

• Outlier rate: 22 / 600 round-judgments = 3.67% (vs ~5% expected under 2σ chance) — point estimate below the ~5% 2σ-chance baseline, but the 95% CI [2.43%, 5.49%] straddles it — the result is consistent with chance, not significantly below it. Note the per-round 2σ trigger did fire on these 22 individual rounds; the rerun verdict below is a class-level judgment, not an absence of tripped triggers.
• Tier-1 skill failures (non-baseline tool with skills_invoked = subagent_dispatches = 0): 0 across the 21 t1–t3 cells with a session-audit.json. t4–t5 session audits were not collected, so this trigger is evaluated over t1–t3 only (not the full n=5 cohort); no audited cell shows a skill failure.
• Outliers by judge: GPT-5.4: 8, grok420: 5, glm51: 4, mimo25pro: 4, opus: 1. Outliers cluster on the panel’s lenience-extreme judges (mimo25pro, GPT-5.4) and on grok420’s root-round drift — these are judge-sampler artifacts, not tool artifacts.

Sample flagged rounds (first 5):

Rerun verdict: no action. No Tier-1 (skill failure, t1–t3 audited) or Tier-3 (harness bug) triggers fired. The Tier-2 per-round 2σ trigger did fire on the individual rounds counted above, but the aggregate outlier rate is statistically consistent with the 2σ-chance baseline (point estimate at/below ~5%, 95% CI overlapping it), so this is treated as a class-level no-action decision rather than per-round re-rolling. Selectively re-rolling the flagged rounds would bias the cohort toward the mean (extreme values re-roll closer to median while in-distribution values stay), shrinking the cohort’s apparent variance without removing real noise. The correct fix for round-level noise is deterministic judge sampling (caveat 09); the correct fix for trial-level variance is more trials per cell (see docs/IMPROVEMENT-PLAN-NEXT-COHORT.md item #1).

Robust-statistics sensitivity (median / trimmed-mean companion)

Sensitivity view: per-tool median and trimmed mean (drop hi/lo) of the 5 trial-level weighted means, instead of the arithmetic mean used above. Rank-1 is invariant on every task under mean / median / trimmed; the largest middle-rank shift in this corpus is gstack refactor (rank-8 → rank-7 under median, driven by one bad trial — the canonical mean correctly retains it). Full table: ../robust-statistics-companion.md; raw figures in ../robust-statistics.json; recompute with scripts/compute-robust-stats.py. Not the pre-registered primary statistic — a sensitivity view alongside the equal-weight companion.

Per-judge z-normalized sensitivity

Tool ordering when each judge is z-normalized ((score − judge_mean) / judge_sd) before averaging — cancels per-judge lenience drift so each judge contributes ordering signal, not absolute lenience. Useful as a sensitivity check against the canonical Weighted Mean: rank-1 should be invariant under both rules.

Δ is Weighted-Mean rank − Judge-Z rank. Δ=0 means the canonical and z-normalized rules agree; |Δ|≥2 means the ordering moves materially under judge normalization (worth investigating).

Comparative-rank validity probe (Opus-1M, parallel signal)

Spearman ρ vs panel weighted-mean rank: -0.405 (n_cells = 25 comparative-judge runs; 5 tools flagged with |Δrank| ≥ 2)

Independent of the per-artifact panel above: one Opus-1M call ranks all 8 tools’ artifacts for a (task, trial) cell side-by-side, then averaged across 5 rounds with fresh per-round Greek-suffix labels and shuffled prompt order. Comparative-rank is a parallel signal — it does NOT enter the weighted mean. High ρ means both judgment regimes agree on tool ordering; low or negative ρ flags a calibration disagreement worth investigating (panel sees artifacts in isolation and can drift; comparative sees the cohort range and recalibrates each round). This in-report table is the Opus-1M lane only, shown as a quick signal; the full two-lane (Opus-1M + GPT-5.4) triangulation with all three pairwise Spearman ρ per task lives in _comparative-eval/_triangulation.md (omitted from public release). Methodology and per-round outputs: _comparative-eval/ (omitted from public release).

[!] Blinding observations volunteered by Opus (rounds where the judge noted potentially-identifying patterns — treat as soft warnings):

• t1/round4: No confident identification. Weak, non-actionable signal only: R4-Zeta’s unusually large multi-file footprint (639+/270-) suggests a heavier agentic harness, but this is speculative and I did not rank on suspected identity.
• t2/round2: No confident identification. Theta’s large multi-file churn (776/274, 10 files) and Beta’s cross-cutting shared-policy refactor suggest heavier-orchestration tooling, but I cannot map either to a specific named tool with confidence, so I am
• t3/round1: R1-Theta’s large multi-file rewrite (10 files, +737/-274) is characteristic of a heavier autonomous-orchestration tool, but this is weak speculation, not a confident identification. No other label exposed identifying style.
• t5/round3: Low confidence. Gamma’s 6-file/710-line footprint suggests a heavier multi-agent orchestration setup, and Beta’s two-layer engine+inventory fix suggests a more exploratory agent, but neither is specific enough to name a tool. No definitive
• t5/round5: No confident tool identification. Mild generic observation only: Delta’s outsized 710/272-line, 6-file diff for a narrow bugfix suggests a more refactor-prone agentic setup, but this is not specific enough to name a tool, and I did not let

Δ = panel_rank − comparative_rank. Positive Δ means comparative ranks the tool higher than the panel; ⚠ marks |Δ| ≥ 2.

Ranking (Weighted Mean)

1. claudekit — 178.93/200
2. ecc — 172.31/200
3. pure — 169.53/200
4. superpower — 166.41/200
5. compound — 166.25/200
6. bmad — 165.72/200
7. omc — 164.80/200
8. gstack — 159.97/200

Per-Trial Breakdown

Weighted-mean score for each individual trial (same 3·opus + 2·GPT-5.4 + others weighting as the canonical column). Surfaces trial-to-trial drift inside a tool — a wide spread means the cohort mean is averaging over disagreeing runs rather than stable ones. The Δ column is max − min across all trials; ≥ 15 pts is flagged as noisy (the tool’s output is bimodal at this sample size).

Reading: a noisy flag here means the cohort mean for that tool is averaging over runs that disagree by ≥ 15 weighted pts. Use this column to read the headline rank with calibration — a tool whose trials cluster tightly is a more reliable signal than one with a wide spread. The pre-registered rerun protocol triggers on per-round outliers within a trial (not trial-to-trial), so a wide Δ here is real tool variance, not a harness artifact.

Skills (t1/t2/t3/t4/t5) = number of distinct skill / slash-command invocations per trial (from session-audit.json → skills_invoked). Subagents (t1/t2/t3/t4/t5) = sub-agent dispatches per trial. A tool whose primary mechanism is a skill/sub-agent and reads 0 for a trial likely failed to invoke its mechanism — under the rerun protocol this is a Tier-1 trigger (“Skill failure”), distinct from the statistical-outlier trigger. Cross-reference these counts when a trial scores far from its siblings.

Per-Judge Means

Each cell is one judge’s mean score for one tool, averaged over that judge’s 15 samples (5 trials × 3 rounds). The columns:

• Tool — same 8 setups as above, ordered by Weighted Mean (rank-1 first).
• opus / grok420 / glm51 / GPT-5.4 / mimo25pro — that judge’s mean score (0–200 rubric) for this tool. Reads vertically to expose judge base-rate effects: GPT-5.4 consistently scores ~20–30 pts below the panel mean (harshest in the panel), mimo25pro ~5–15 pts above (most lenient, occasionally saturates at 200). Reads horizontally to see whether the judges agree on the ordering: if a tool is rank-1 under one judge and rank-7 under another, the consensus is weak.

Provenance Defects (2)

Files whose internal judge field disagrees with the filename slot the aggregator dispatched by. The score is still counted (not retroactively pulled); listed here for transparency.

• _blind-eval/Tango/gpt54pro-judge.json — slot gpt54pro but .judge = openai-o3
• _blind-eval/Tango/round1/gpt54pro-judge.json — slot gpt54pro but .judge = gpt-5