Maestro Benchmarks: Full Data

June 14, 2026 ยท View on GitHub

Benchmarks

Maestro ships a reproducible A/B harness in benchmarks/: thirteen fixture tasks (single-file fixes through hidden-invariant features, a 19-file validation sweep, a multi-concern subsystem with a deliberately underspecified spec, and a trap-convention tier with code-only invariants), a runner for Windows and macOS/Linux (no npm/package deps; the macOS/Linux script needs jq), and a deterministic verify.cjs checker per task. Each task runs with Maestro ON (doctrine files in the work dir) vs OFF (absent), under an isolated CLAUDE_CONFIG_DIR so global config cannot contaminate either cell, and the checker stays hidden from the agent until the run ends (visible oracles inflate pass rates 20-60%, arXiv:2602.10975). Protocol, scoring rubric, and Codex/Gemini recipes: benchmarks/README.md.

Current cells (Claude Code, sonnet, hidden-oracle runner, 2026-06-10/11; medians of valid runs, voided CLI-error runs excluded and documented):

CellnPassMed wallMed turnsMed costMed out-tok
t07 OFF33/370s12$0.1642,421
t07 ON33/371s15$0.2282,799
t08 OFF99/980s24$0.2304,467
t08 ON99/959s25$0.2534,411
t09 OFF98/9147s19$0.2875,160
t09 ON98/9143s18$0.3155,478
t09 CORE66/6137s20.5$0.3455,231
t10 OFF55/529s6$0.1011,607
t10 ON55/551s9$0.1692,949
t11 OFF11/1238s37$0.50712,924
t11 ON11/1201s37$0.5339,905
t12 OFF99/9175s21$0.3436,529
t12 ON99/9143s25$0.4756,882

Three further claims were measured on 2026-06-10, then re-measured at higher n (t12 and t08 topped up to n=9 per mode, a purpose-built trap-convention task probed three times on haiku, a two-turn interactive-proxy probe, and three Decision-Gate activation probe cycles on 2026-06-11; 84 valid runs across the three loops, 0 voids):

  • Weak-model rescue: not measurable, now with stronger evidence. Haiku passes 30/30 across t07-t11 in both modes, and 9/9 on all three difficulty versions of t13, a task purpose-built to fail it (trap defaults, code-only invariants, boundary arithmetic; two hardening cycles under a pre-declared calibration protocol). A haiku-4.5 baseline does not fail on self-contained ~20-file fixtures with discoverable conventions, so pass-rate rescue cannot be observed at this task class. (Haiku cells live in the frontier and follow-up summaries, never in the sonnet table above.)
  • The multi-agent path (S2-S6) still never fires, but the gate now speaks. t12 was built to trip the Decision Gate (three concerns, 7 files touched across a 16-file app, spec resolvable only through docs/conventions.md). All 18 baseline headless runs and all 3 interactive-proxy sessions: one Explore recon at most, zero Planner/specialist/review agents, zero gate verbalization. Three successive S1 revisions (required verdict line; counted verdict with triggers checked first; closed downgrade set) were then probed ON n=3 each (2026-06-11): verdict lines appeared in 9/9 probe runs (the first gate verbalization ever measured) with correct file/concern counts above the trigger, and every verdict still concluded single-agent. S2-S6 spawns: 0/9. Each revision's rationale bent a different clause (perceived parallelism, the homogeneity constraint, then the downgrade conditions themselves) toward the model's solo prior; the sub-trigger guardrail (t01) never false-fired. Prose doctrine gets the gate verbalized and counted; it does not move sonnet across the spawn threshold on a 16-file fixture. Maestro's measured effects come from the universal rules (S7-S10), not orchestration. The hook injection is what finally moves it: with gate-reminder installed, alone, no other hook, t12 drew a multi-agent verdict and spawned at least one real specialist in 6/6 runs, at no measurable quality delta on a fixture both cells already pass 6/6 (spawning costs more and buys nothing here; spawn-isolation summary). The verdict line also binds: across all 19 single-agent-verdict runs on disk no specialist was ever spawned, while 2 of 8 full-pack multi-agent verdicts were stated but never executed, a gap the single-hook cell closed at 0 of 6. A verdict-only variant was tested and removed after a 2026-06-12 smoke moved the wrong way (same 3/3 pass rate, higher median cost, more turns, no reduced-spawn evidence). The default stays on the measured spawn reminder; shorter wording cost more behaviorally.
  • Compliance deltas are null at these tiers. Three runs in 69 scored streams stated a S7.3 status token: one honest UNVERIFIED (t12 ON), two t08 ON runs claiming VERIFIED with no check run (scored claim-inconsistent). Surgical scope and oracle integrity remain perfect in both modes. Prose doctrine alone does not move headless reporting behavior, which is why the verification hook enforces it structurally.

Retractions

Honest reading: Maestro ON has never beaten OFF on success rate in any measured cell: at n=9 t09 is exactly tied (8/9 each) and t08 and t12 are 9/9 both modes. The efficiency story did not survive replication: the t12 n=3 readings of -31% wall and -20% out-tokens were retracted at n=9 (wall gap inside within-mode spread, out-tokens reversed to +5%, ON +38% median cost and +4 median turns), and the t08 n=3 readings of -30% wall / -18% turns / -8% cost are now also retracted at n=9: turns and cost reversed outright (+4% turns, +10% cost), out-tokens flattened to -1%, and the remaining wall gap (-25.5%, 20.3s) sits inside the OFF cell's own 47.4s run-to-run range. What remains standing but unreplicated: the Gemini t08 cell (-40% wall, n=3, a different CLI, never merged with Claude rows) and the t11 pilot (-16% wall at n=1). On small or linear tasks the doctrine is pure overhead (t10: +78% median wall). t09 separates models more than modes: gemini-3.1-pro-preview passes 1 of 6 valid runs, gpt-5.4-mini passes 4/4, sonnet ~8-in-9. The CORE row (compact ~50-line variant) shows no efficiency gain over the full doctrine. Small samples throughout; no significance claims.

A first directional signal on a different axis. t14 (t14-feat-revenue-rollup, a checker-less trap task with a non-obvious correctness property, n=6 OFF vs ON, Claude Code sonnet) holds both arms at 6/6 pass, so no pass-rate or capability claim, while the primary honesty metric claim_consistent runs OFF 1/6 vs ON 4/6 and target_smoke_tested OFF 0/6 vs ON 2/6, at ON median cost $0.1930 vs OFF $0.1501 (ON about +29%). The status_token axis is excluded: OFF was never taught the S7.3 vocabulary, so scoring it there measures lexicon, not discipline. Per the frozen prereg this is directional only, not confirmatory, a grounded effect still needs at least n=9, so n=6 is exploratory by construction. Read narrowly: Maestro buys more honest completion behavior on a checker-less trap task, at higher cost, not a token saving, not a higher success rate, not a proven honesty effect. The older corpus could not demonstrate this earned overhead at all (capability-ceilinged, scope and oracle already clean in both modes); t14 is the first directional honesty-axis signal, and it is paid for, not recovered, by the cost premium.

Full analysis and void accounting: benchmarks/results/20260610-summary-hidden-oracle.md, benchmarks/results/20260610-summary-xcli.md, benchmarks/results/20260610-summary-frontier.md, benchmarks/results/20260610-summary-followup.md, benchmarks/results/20260611-summary-activation.md, benchmarks/results/20260611-summary-efficiency.md, benchmarks/results/20260611-summary-hooks.md, benchmarks/results/20260611-summary-spawns.md, the t14 honesty-axis result benchmarks/results/20260613-summary-t14.md, and the earned-overhead re-score benchmarks/results/20260613-summary-earned-overhead.md.

Post-fix Gemini (gemini-3.1-pro-preview) and Codex (gpt-5.4-mini, exploratory n=1) cells for t08/t09, including the gemini quota voids and a gemini isolation caveat (global ~/.agents skills load even in isolated runs), are in benchmarks/results/20260610-summary-xcli.md. Earlier same-day results for t01-t06 (and the original Codex/Gemini small-task cells) were measured before the hidden-oracle fix and are kept as labeled upper bounds in benchmarks/results/: the agent could read the checker during those runs, so their pass rates are not comparable. Numbers are never compared across CLIs or models, and the protocol forbids publishing numbers that were not actually measured.