From Faulty Claims to Incorrect Patches: An Empirical Study of Task-Grounded Reasoning in Coding Agents

You Wang, Jinjun Huang, Zhonghao Jiang, Jialun Cao, Jiachi Chen +1

Large language model (LLM)-based coding agents increasingly resolve real-world software engineering tasks, yet still fail on many of them. Prior trajectory studies characterize these failures through task-agnostic patterns of agent behavior, such as repeated actions. However, an agent may follow a sound workflow yet act on a faulty understanding of the existing code, the task requirements, or a candidate fix. Reasoning models, which power most state-of-the-art coding agents, reveal this understanding in their native chain-of-thought (CoT), whose claims about the task can be checked against the facts. Yet it remains unclear how many such checkable claims the CoT contains, how many are faulty, and whether faulty claims actually cause incorrect patches. We present the first empirical study of such claims, which we term task-grounded reasoning, analyzing 400 trajectories of four open-weight reasoning models on SWE-bench Pro. We extract atomic claims from the CoT with an LLM, assess them against evidence gathered by an LLM agent, and manually trace the defects of incorrect patches back to faulty claims, confirming each link with a claim-corrected replay. Task-grounded reasoning covers…