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English(EN) Can Coding Agents Sustain Continual Evolution without Forgetting?

编码代理在持续演进中遭受灾难性遗忘

作者 PulseAugur 编辑部 · [2 个来源] ·

一项新的实证研究调查了在持续自我演进的编码代理中灾难性遗忘的挑战。研究人员发现，随着代理演进以处理新任务，它们在先前学习任务上的表现会显著下降。例如，一个使用 DeepSeek-V4-Flash-0731 和 Meta-Harness 的代理在 FeatureBench 上演进后，在 SWE-Bench 上的表现急剧下降。研究结果强调了在不损失先前任务性能的情况下实现持续自我演进的方法的必要性。 AI

影响 凸显了当前 AI 代理演进中的一个关键限制，表明需要新的技术来防止先前学习任务上的性能下降。

排序理由 该集群包含一篇详细介绍 AI 模型行为实证结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

编码代理在持续演进中遭受灾难性遗忘

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该集群包含一篇详细介绍 AI 模型行为实证结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Can Coding Agents Sustain Continual Evolution without Forgetting?

    Recently, harness self-evolution has emerged as an important paradigm for improving agent performance. Prior studies show that evolving the agent harness can effectively enhance agent capabilities without modifying the foundation model's parameters. We find that existing approach…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Can Coding Agents Sustain Continual Evolution without Forgetting?

    Empirical study shows continual harness self-evolution across software engineering tasks causes severe catastrophic forgetting, with performance on earlier datasets dropping sharply after later evolution.

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