Ecdysis：LLM Agent运行时通过模式学习而非单点修复提升性能
If an agent rewrites its runtime for every bad decision, it can learn the wrong…
Agent系统容易陷入“头痛医头”的陷阱，这篇论文提出的从单点修补转向模式识别的思路很有启发性，做Agent基建的同学值得参考其设计权衡。
If an agent rewrites its runtime for every bad decision, it can learn the wrong lesson
如果代理因每次错误决策而重写其运行时，它可能会学到错误的教训
Fix recurring failures across tasks, not every failure.
修复跨任务中反复出现的失败，而不是针对每一次失败。
Agent runtimes should learn from failure patterns, not failure counts: ECDYSIS grouped recurring problems across tasks and delivered higher accuracy, faster training, and stronger cross-model transfer.
代理运行时应从失败模式中学习，而非失败次数：ECDYSIS 跨任务对重复出现的问题进行分组，从而实现了更高的准确率、更快的训练速度和更强的跨模型迁移能力。
Patch every miss, and you can accidentally hard-code one model's bad habits into the system.
修补每一个疏漏，你可能会无意中将该模型的坏习惯硬编码到系统中。
ECDYSIS instead looks for the same kind of failure across different tasks before deciding the harness itself needs fixing.
相反，ECDYSIS 在决定是否需要修复运行环境本身之前，会先寻找不同任务中同类别的失败。
– arxiv. org/abs/2609.11677
– arxiv.org/abs/2609.11677
Title: "Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents"
标题：《Ecdysis：LLM 代理运行时环境的高效有效训练》
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