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论文精选 65arXiv

CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents· CodeRescue: 预算校准的编码代理恢复路由

Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an incorrect answer. Existing cost-aware systems typically treat such failures as cascade decisions: try a cheap model first, then escalate hard cases to a stronger and more expensive model. In coding, however, execution feedback can also make further cheap-model recovery worthwhile, raising a budgeted deployment question: when should an agent spend more cheap compute, and when should it escalate? We formulate this post-failure decision as recovery routing over heterogeneous actions and train a supervised router from execution rollouts. To make the same router usable under changing budgets, we add a Conformal Risk Control (CRC) layer that selects a deployment

AI 解读论文

针对编码代理在执行失败后的预算校准恢复机制

核心方法
提出CodeRescue框架,利用监督学习训练恢复路由模型,并通过Conformal Risk Control(CRC)层适应不同的预算条件
适合谁读
研究者、工程师
要解决的问题
编码代理在遇到执行失败时如何在有限预算下做出最优的恢复决策
关键实验
包括在多个编程任务上的实验,验证了模型在不同预算条件下的性能
主要贡献
为编码代理提供了动态调整恢复策略的方法,提高了在不同预算下的执行效率和成功率
意义与局限
有助于优化编码代理在真实环境中的应用,特别是在资源受限的场景下;但该方法在复杂环境中的泛化能力尚需进一步验证
领域:cs.AI作者:Qijia He、Jiayi Cheng、Chenqian Le
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