feat(loops): anytime metrics — time-to-satisfactory from the waterfall, free#253
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…l, free Derived entirely from existing spans (no new instrumentation): per-task hill-climb curves (best-so-far score after each shot with elapsed wall and cumulative spend) and the standard anytime-optimization summary per (strategy, satisficing target): median time-to-target, shots-to-target, COCO ERT (Σ all wall-time including failures / #successes — the honest all-in cost per success), $ /success, and the AUC of the anytime curve with a sparkline render. Satisfaction follows the COCO/BBOB convention — a SET of satisficing targets measured independently (targets: [0.5, 0.8, 1]) — or per-task bars via targetFor (task-generic satisfaction). steering-modes prints the table per arm; per-model comparison = arms with different WORKER_MODEL.
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✅ Auto-approved PR — 5fa9d3f3
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tangletools · auto-approval · reason: blanket_auto_approve · 2026-06-11T00:16:09Z
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The answer to 'time until satisfactory output, measuring the hill-climb at each step' — and we get it free: derived entirely from the waterfall's existing spans, zero new instrumentation.
Standard vocabulary (per the PLAIN.md rule — no invented terms)
targets: [0.5, 0.8, 1]measured independently (the COCO multi-target convention), or per-task bars viatargetFor— satisfaction generic to the task.Wired into
steering-modes(prints per arm — tonight's run produces the curves per steering mode; per-model = arms with differentWORKER_MODEL). +3 tests (multi-target hits, ERT failure-charging, per-task bars). Suite 788 ✓.