AI RESEARCH
Ask or Assume? Uncertainty-Aware Clarification-Seeking in Coding Agents
arXiv CS.CL
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ArXi:2603.26233v1 Announce Type: new As Large Language Model (LLM) agents are increasingly deployed in open-ended domains like software engineering, they frequently encounter underspecified instructions that lack crucial context. While human developers naturally resolve underspecification by asking clarifying questions, current agents are largely optimized for autonomous execution. In this work, we systematically evaluate the clarification-seeking abilities of LLM agents on an underspecified variant of SWE-bench Verified.