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    HarnessEvolve Addresses Self-Evolving Agent Failures

    DAIR.AI shares an arXiv paper on fixing three failure modes in self-evolving agents.

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    3 Sources, 28d ago, first seen 28d ago

    TLDR

    DAIR.AI posted about HarnessEvolve by Wen Jiang and colleagues. The account listed three failure modes that affect self-evolving agents. Terminal-only feedback creates ambiguity about which step caused an error. Agents memorize task-specific patterns instead of acquiring general capability. Unguarded updates can erase existing competencies. HarnessEvolve learns from reference trajectories to handle all three issues. The arXiv entry states that self-evolving agents optimize prompts, skills, tools, and execution logic from environmental feedback, though the approach remains limited by the listed problems.

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    3 Sources, first seen 28d ago

    Combined views

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    3 Sources, first seen 28d ago

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    27 comments
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    61 reposts

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    3 Sources

    @dair_aiNice paper with great insights on improving self-evolving agents. Self-evolving agents fail in three specific ways: 1. Terminal-only feedback makes it ambiguous which step caused the error. 2. Agents memorize task-specific patterns instead of acquiring general capability. 3. And unguarded updates quietly erase competence the agent already had. HarnessEvolve addresses all three in one loop. For credit assignment it generates reference trajectories, execution paths produced when the agent is given the ground-truth answer, then aligns failed runs against them to extract error signals. Those signals are clustered so the update targets a systematic pattern rather than one bad rollout. Two gates stand between a candidate harness update and the live agent. A quality gate filters data leakage and prompt bloat. A performance gate accepts the update only if it improves the current batch without degrading recent batches, with epoch-end validation on a held-out set choosing the snapshot. Execution, evaluation, optimization and gating are separate modules, so the agent doing the work is decoupled from the pipeline changing it. Results hold across open-domain and enterprise benchmarks, different models and different agent frameworks. Paper: https://arxiv.org/abs/2609.00829 Chat with Paper: https://academy.dair.ai/papers/harnessevolve-learning-from-reference-trajectories-for-reliable-agent-self-evolu-2609.00829
    @rohanpaul_aiSelf-improving agents have a basic problem: when a long run fails, they often do not know which step actually caused it. If an agent is going to improve itself, it needs more than failure feedback: HarnessEvolve treats agent self-improvement like software debugging: find where a failed run first went off track, fix the recurring cause, then reject any edit that breaks existing behavior. It clusters those errors into recurring patterns and can edit the whole agent harness: prompts, skills, tools, scripts, and execution logic. On CloudCoreNetwork-QA with Qwen3.6-27B, full HarnessEvolve reached 86.9% accuracy; removing reference trajectories dropped it to 57.8%. The full system also beat the strongest baseline there by 21.6 percentage points. Candidate edits then face gates for training-data leakage, prompt bloat, regressions on recent batches, and held-out validation. – arxiv. org/abs/2609.00829 Title: "HarnessEvolve: Learning from Reference Trajectories for Reliable Agent Self-Evolution"

    3 Sources

    @dair_aiNice paper with great insights on improving self-evolving agents. Self-evolving agents fail in three specific ways: 1. Terminal-only feedback makes it ambiguous which step caused the error. 2. Agents memorize task-specific patterns instead of acquiring general capability. 3. And unguarded updates quietly erase competence the agent already had. HarnessEvolve addresses all three in one loop. For credit assignment it generates reference trajectories, execution paths produced when the agent is given the ground-truth answer, then aligns failed runs against them to extract error signals. Those signals are clustered so the update targets a systematic pattern rather than one bad rollout. Two gates stand between a candidate harness update and the live agent. A quality gate filters data leakage and prompt bloat. A performance gate accepts the update only if it improves the current batch without degrading recent batches, with epoch-end validation on a held-out set choosing the snapshot. Execution, evaluation, optimization and gating are separate modules, so the agent doing the work is decoupled from the pipeline changing it. Results hold across open-domain and enterprise benchmarks, different models and different agent frameworks. Paper: https://arxiv.org/abs/2609.00829 Chat with Paper: https://academy.dair.ai/papers/harnessevolve-learning-from-reference-trajectories-for-reliable-agent-self-evolu-2609.00829
    @rohanpaul_aiSelf-improving agents have a basic problem: when a long run fails, they often do not know which step actually caused it. If an agent is going to improve itself, it needs more than failure feedback: HarnessEvolve treats agent self-improvement like software debugging: find where a failed run first went off track, fix the recurring cause, then reject any edit that breaks existing behavior. It clusters those errors into recurring patterns and can edit the whole agent harness: prompts, skills, tools, scripts, and execution logic. On CloudCoreNetwork-QA with Qwen3.6-27B, full HarnessEvolve reached 86.9% accuracy; removing reference trajectories dropped it to 57.8%. The full system also beat the strongest baseline there by 21.6 percentage points. Candidate edits then face gates for training-data leakage, prompt bloat, regressions on recent batches, and held-out validation. – arxiv. org/abs/2609.00829 Title: "HarnessEvolve: Learning from Reference Trajectories for Reliable Agent Self-Evolution"