| # Calibration status (v0.2.0) |
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| The expert tier separates mid-size and open models clearly, but it does not separate frontier agents: they solve |
| almost every expert task, usually in a few minutes. v0.2.0 makes no frontier-difficulty claim. |
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| ## What the v0.2.0 leaderboard is |
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| - One attempt per task and model on the 15 tasks of `3ambench-expert-lb@0.2.0` (3 per family), run locally through |
| Harbor on Docker Desktop with the agent's network enabled. These are development runs, not a sealed, held-out |
| comparison. |
| - Every stored run was graded with the release grader. The fictional company domains were renamed to reserved |
| `.example` names after the runs; stored workspaces and trajectories are read through that rename, and all 171 |
| stored runs (96 expert, 73 core, plus 2 excluded) regrade to the same scores they had before it. |
| - Coverage differs by model and the table says so: two `afx-e5-s08` attempts (Opus and Astra) are excluded because |
| that task's survey wording was corrected after they ran; Astra and Sol were rerun on the corrected task, the other |
| models were not. gpt-oss-120b ran on 10 tasks because its hosted API had a spending cap. |
| - Every failure in these runs was triaged before it was counted. Failures caused by the task or the grader |
| (an unstated requirement, scenario data that punished a valid fix, ambiguous wording) were fixed in the |
| generator or grader and the stored runs regraded; only genuine model mistakes count as failures. |
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| ## Rules for counting results |
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| - Mechanical acceptance gates come before any model measurement: the reference solution scores 1, an alternative |
| valid solution scores 1, doing nothing scores 0, a partial solution lands in its band, and every scripted cheat |
| stays in its band. |
| - A lower score caused by ambiguity, grader errors, missing evidence or infrastructure is not a model failure and not |
| a hardening result. |
| - `scripts/expert_calibrate.py` requires a balanced pilot before any difficulty verdict: two declared models, three |
| preselected tasks per family and three valid attempts per model and task. Missing cells produce `collect`, not a |
| verdict. Duplicate and invalid runs never count as extra attempts, and historical results are regraded against |
| the current task artifacts before use. Report models separately: a pooled average can hide saturation by the |
| stronger model. |
| - Before fresh measurements, freeze task and grader hashes, seeds, model and harness settings, reasoning and |
| sampling settings, resource, tool and network limits, attempt counts and the retry and exclusion rules, and keep |
| every raw trajectory, final workspace, result and exclusion. |
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| ## Frontier tier |
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| A harder frontier tier is in development. It is held out of this release so that it can be measured on tasks no |
| model has seen; it will ship as its own version once a calibrated measurement supports it. |
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