HarnessEvolve paper: dual-gate loop fixes three failure modes of self-evolving agents
dair_ai · x · 2026-09-03
dair-ai highlights a new paper on self-evolving agents, which fail in three specific ways: terminal-only feedback makes step-level error attribution ambiguous; agents memorize task-specific patterns instead of general capability; and unguarded updates quietly erase existing competence.
HarnessEvolve addresses all three in one loop:
- Credit assignment: generate reference trajectories (execution paths when the agent is given the ground-truth answer), then align failed runs against them to extract error signals.
- Signal clustering: updates target systematic error patterns rather than one bad rollout.
- Dual gates: a quality gate filters data leakage and prompt bloat; a performance gate accepts a harness update only if it improves the current batch.
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