Archived
2026-09-09ExecCritic: Learn to Test, Test to Improve for Coding Agents
Leitian Tao, Baolin Peng, Haorui Wang, Hang Wang, Hao Cheng, Wenlin Yao, et al.
arXiv:2609.09133v1When coding agents try to fix a bug, they often rely on tests to tell them whether a patch is actually correct. The problem is that if the agent writes both the code and the tests, the tests can be wrong in the same way as the patch, creating false confidence. ExecCritic tackles this by splitting the job into two roles: one agent learns to write behaviorally valid tests, and a separate repair agent uses those tests to improve the code without changing them. The authors train both roles with a reinforcement learning recipe built around test, verify, and revise. On SWE-bench Verified, better tests clearly lead to better repairs, and the full system beats a no-test baseline by a meaningful margin. Why it matters is simple: software agents are only as good as the feedback they trust. By making test generation itself a learnable skill, ExecCritic moves coding assistants closer to dependable, self-improving debugging.
Also spotted that day
TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action ModelLearning Length-Extrapolatable Recurrent ModelsReCite: Agentic Reasoning for Faithful CitationProcedural Graphs: Self-Evolving Execution Structures for LLM AgentsSilver Rate Is (Almost) Optimal for Gradient Descent AccelerationCopying explains the collective behavior of AI agents in the wildStudying Image Tokenizers as Visual Languages in Unified Multimodal ModelsNOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and ForecastingA Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare ProcessesEntropy-Regularized Rank-Masked Policy Optimization for Test-Time Reinforcement Learning in Code GenerationCo-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation FailsNearly Tight Rademacher Bounds for Sparsely Activated Neural NetworksA Generalization of Amari's Bayesian DualityCanonical Color as a Lens into Concept Decodability in Vision Encoders and VLMs
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