ouroboros/devtools/benchmarks
Ouroboros b463bb3d93 Add the Z.ai (GLM) direct provider with effort projection at the send boundary
zai:: joins the direct providers exactly the way deepseek:: did: prefix and
credential registry, ZAI_API_KEY plus a ZAI_PLAN endpoint selector
(provider_models.resolve_zai_base_url: empty/payg = api.z.ai/api/paas/v4,
coding = the Coding Plan endpoint), the routing target, live catalog fetch,
provider Test, settings card, onboarding contract, review-fallback roles,
single-provider startup and review detection, secret masking, benchmark
env hygiene, and docs.

Reasoning effort now reaches Z.ai. The provider serves an ABSENT
reasoning_effort at its maximum tier, so every call on the old generic
compatible route was billed at max regardless of the configured effort.
The canonical scale is projected onto Z.ai's own low/high/max enum
(ZAI_REASONING_EFFORT_ALIASES: none/minimal -> low, medium -> high,
xhigh/ultra -> max), disclosed as reasoning_effort_clamped when the tier
changes; GLM-5.3 rejects every other value and cannot disable thinking
(HTTP 400 code 1210), and forced tool_choice works with thinking on, so
there is no DeepSeek-style suppression arm. The projection is keyed on the
provider id the owner configured, never on a model name: a GLM served
from an owner's own OpenAI-compatible endpoint keeps today's behavior.

The provider port is the contributor's own work from the closed PR #1194,
narrowed to Z.ai (the DashScope and Moonshot lanes were not measured and
stay out). 07-configuration gains two settings rows and one route
paragraph (budget 37300 -> 38400), 02-naming records the dated Z.ai
probe in the external-fact inventory, and the onboarding bootstrap
fixture and data-layout inventory are regenerated.

Co-authored-by: josephsteuerjr <josephsteuerjr@gmail.com>
2026-09-25 17:41:54 +03:00
..
common Add the Z.ai (GLM) direct provider with effort projection at the send boundary 2026-09-25 17:41:54 +03:00
continual_learning WIP: preserve #1196 continuity repairs before upstream integration 2026-09-24 05:09:55 +03:00
cowork_bench fix: bind Cowork evaluator score to run protocol provenance 2026-09-25 07:09:54 +03:00
cybergym fix: close remaining frozen release gate regressions 2026-09-18 13:28:04 +03:00
editbench Fix benchmark actor provenance 2026-08-20 02:32:32 +03:00
gaia Merge upstream ouroboros 23ab428f into the v7 line: absorb 407 commits into the module split 2026-09-04 19:32:55 +00:00
harness_bench_fast Persist complete fixed benchmark actors 2026-08-20 04:25:13 +03:00
osworld ouroboros: checkpoint after task c6207d8d665040b0 — Долгая работа #1196 — продолжение до реализации 2026-09-23 23:52:17 +03:00
programbench Add the Z.ai (GLM) direct provider with effort projection at the send boundary 2026-09-25 17:41:54 +03:00
swe_bench release 6.100.0: delegated runs execute in private snapshots — capture, disposition, and GC carry one honest truth (sprint phase C) 2026-08-12 17:54:10 +03:00
swe_bench_pro Consciousness: review round 1 fixes (alarm re-arm exclusions, graceful wake ceiling, own cards, honest status) 2026-09-16 10:09:18 +03:00
terminal_bench Add the Z.ai (GLM) direct provider with effort projection at the send boundary 2026-09-25 17:41:54 +03:00
__init__.py feat(devtools-benchmarks): add official benchmark harnesses and workspace executor 2026-06-06 12:03:30 +03:00
evolve_smoke.py swe-pro: e1v2 evolution harness (replaces evolve_pro) 2026-06-11 16:10:31 +03:00
README.md fix(benchmarks): retain official evaluator evidence independently of execution 2026-09-25 03:21:08 +03:00

Ouroboros Benchmark Devtools

This directory contains tracked operator tooling for reproducible benchmark work. These files are reviewed when touched, but are not imported by the runtime core and are not packaged as app runtime code.

Integrations

  • terminal_bench/ — Harbor installed-agent adapter for Terminal-Bench 2.1. Use run_tb.py for leaderboard-shaped k-trial runs and submission layout; use run_harbor_smoke.py for small local smoke runs.
  • osworld/ — OSWorld 2.0-aligned step-loop adapter (pinned xlang-ai/OSWorld-V2@c261cb57, 500-step budget, official show_result.py result layout, VM-state-aware prompting + terminal final_answer audit capture) plus logs-only audit tooling; runnable against a local vmware/docker OSWorld checkout, cloud providers and checkpoint curves not implemented — see osworld/METHODOLOGY.md.
  • swe_bench_pro/ — SWE-bench Pro patch capture/grading. Frozen prepared repos use pro_predictions.py; persistent evolutionary runs use e1v2/run_pro.py / e1v2/auto_run.py.
  • swe_bench/ — standard SWE-bench prediction helpers.
  • programbench/ — ProgramBench cleanroom runner (run_programbench_e2e.py for end-to-end Ouroboros harness runs; run_programbench.py prepare/package-only).
  • continual_learning/ — launcher wrapper for the EXTERNAL CL-Bench (continual-learning-bench.com) runner: strictly sequential memory / continual-learning runs against a live Ouroboros agent (evolution off). Use run_clb.py; the runner repo + its src/systems/ouroboros/ adapter are obtained separately (see its README).
  • cybergym/ — Level-1 CyberGym adapter for the pinned sunblaze-ucb/cybergym source. Use run_cybergym.py for the binary-only, private-sidecar protocol; see its README and METHODOLOGY for the exact model, provider, final-PoC, and denominator contract.
  • harness_bench_fast/ — Ouroboros CLI wrapper and methodology notes for the public ai-forever/harness-bench-fast runner.
  • cowork_bench/ — pinned Cowork Bench task-container adapter, persistent MCP sessions, campaign spending and resource limits, plus an offline evidence audit; see its methodology for protocol differences.
  • common/ — shared manifests, result ledgers, safe run roots, secret hygiene, and official command builders.

Output Roots

Write generated run artifacts under an explicit benchmark output root outside repo/ and outside live runtime data/, typically /Users/anton/Ouroboros/bench_runs/.... Tests must set OUROBOROS_BENCH_RUNS_ROOT to a temporary directory so local test runs do not pollute real benchmark bundles.

CLEAN SEED IS MANDATORY FOR SUBMITTABLE RUNS

BEFORE STARTING ANY SUBMITTABLE / LEADERBOARD RUN, THE SEED WORKTREE MUST BE CLEAN: git -C <seed> status --porcelain MUST BE EMPTY AND git describe --dirty MUST NOT CARRY A -dirty SUFFIX. A DIRTY SEED (UNCOMMITTED ADAPTER EDITS, STRAY FILES) MAKES THE RUN MANIFEST RECORD ...-dirty, THE PROVENANCE BECOMES NON-REPRODUCIBLE, AND THE RUN CANNOT BE SUBMITTED — THE MONEY IS BURNED. IF ADAPTER EDITS ARE NEEDED, COMMIT THEM FIRST (A WIP COMMIT IN THE SEED IS ACCEPTABLE IF RECORDED IN THE RUN NOTES), CLEAN UP STRAY FILES, THEN LAUNCH. IF A DIRTY SEED IS DISCOVERED AFTER LAUNCH, ESCALATE TO THE OWNER IMMEDIATELY (STOP VS FINISH IS THE OWNER'S CALL) — NEVER STAY SILENT.

Shared Sidecar Schemas

  • Run manifests record non-secret provenance: requested task ids where the benchmark runner exposes them before execution, requested counts/selection slots for deterministic first-N runs such as Terminal-Bench, exact argv, official command shape, output paths, model slots, the canonical Available-subagents projection where the launcher owns it, source commit, dirty-state counts, and hashes. Defaults are adapter-specific (run_manifest.json, <predictions>.run_manifest.json, or osworld_preflight.run_manifest.json).
  • Result ledgers are denominator-preserving Ouroboros JSONL files. They record every requested instance, including setup failures, timeouts, and empty patches, even when the official benchmark prediction/submission format only accepts successful rows. Defaults are adapter-specific (result_index.jsonl, <predictions>.ledger.jsonl, or osworld_preflight.ledger.jsonl). A row's official_eval_status is unreported unless its adapter states one; not_run is an explicit claim that the official evaluator never ran.

These sidecars are audit artifacts, not replacement scoring. Official benchmark harnesses and official result files remain the scoring authority.

Bench-Template Scaffold Defaults (v6.55.0)

All committed bench settings templates share these disclosed defaults:

  • Cost pacing (v6.56.0): tasks with a finite budget receive latched in-task COST milestones (50/25/10% remaining + ~80%-spent wrap-up note) from the task_pacing SSOT; budget_profile.cost_hard_stop_pct=0 (SWE-Pro/PB profiles) disables the in-task hard stop AND the wrap-up affordability rail, so deadline/rounds own the bounds.

  • OUROBOROS_MAX_WORKERS=4 — same-model subagent slots for decomposition WITHIN one task (the root agent takes one lane). Never independent attempts with selection, so pass@1 claims hold. Fixed-model profiles serialize exactly one api_model row in OUROBOROS_SUBAGENTS; they cannot inherit a default Light scout, second family, or agent-session route. The core default (10) is untouched.

  • OUROBOROS_SAFETY_MODE=light — bench containers/rendered data roots are disposable jails; deterministic guards stay, the LLM safety pass is kept for integration tools only. User defaults are untouched.

  • OUROBOROS_RUNTIME_MODE=pro for CONTAINER benches (Terminal-Bench, SWE-bench Pro, ProgramBench, OSWorld). GAIA stays light: its solver runs without workspace isolation against a live repo, so pro would grant benchmark prompts write authority over the system body.

  • claude_code_edit disabled in every bench solve task — benches measure the single-model Ouroboros harness; external agent-session delegates are a separate experiment. (D10 retired the tool itself; the legacy name in these configs stays meaningful because disabled_tools=["claude_code_edit"] also withholds the successor delegate_start.)

Per-bench METHODOLOGY files carry the full rationale.

Methodology Rule

Benchmark changes must be general-purpose harness improvements first. Do not add task-specific answers, hidden verifier knowledge, or resource/timeout overrides that violate a benchmark's official submission rules.

Upstream-Drift & Protocol-Fidelity Pre-Flight (MANDATORY before any expensive run)

Four checks, each of which failed at once in the CLB v6.81.0 campaign (continual_learning/METHODOLOGY.md §10-§12) and would have cost the whole budget if the run had been the submission:

  1. Upstream drift. Check the external benchmark repo's commits/PRs/issues AFTER our pin (via GitHub API/web; do not fetch into the pinned clone). A metric/scale change can ship as re-scored reference artifacts with no task-code commit — when reference artifacts carry two reward copies (top-level vs nested), prove which one the local scorer reads before trusting any number it prints.
  2. Empirical protocol fidelity. Verify every protocol parameter by its ARTIFACTS, not its flag. Multi-seed must mean different question orders: diff prompt hashes across seeds BEFORE mass spend (CLB: --run-index was accepted and silently dropped on 4/6 domains — five "seeds" were five replicates). This generalizes the "declared vs applied" settings rule to the harness itself.
  3. Read submission requirements BEFORE the run, not after. Which arms are mandatory (stateless baseline!), how many seeds, which artifact layout, whether a public implementation link is required — and run in submission-shape via the official runner from the start. Money burned on a non-submittable path converts to a submission only by fabricating provenance, which is prohibited.
  4. A pinned local scoring script is not ground truth. Reconcile local normalization against the PUBLIC leaderboard (top-1 value, last-updated date). A mismatch means a convention divergence to be root-caused — not "the other party's numbers are stale". Never compare two systems' raw scores without proving they are on the same scale.

LifelongAgentBench Status

lifelongagentbench (arXiv 2508.19005) has NO adapter here: the external runner is unavailable (only traces from a prior external run exist), and the observed 100% run was a metric artifact — the runner lacked a gold oracle, so e.g. a NULL-primary-key task graded as pass. Do not cite that number. Status: blocked until a real runner with gold labels is obtained.