Author

Hao-Ye Tian

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#artificial intelligence Preprint Oct 2026

Do Tool Calls Execute as Intended? Measuring and Repairing Intent-Execution Correspondence in LLM Agents

Agents built on large language models (LLMs) build and run software through tool calls. A call reaches its program through several hops, and any hop can change the call without notice. When the changed call fails, the agent retries a correct call, which costs users time and money. Benchmarks and failure analyses do not...

Bo-Yang Yang, Zhen-Hao Li, Zi-Yao Yang et al. · 0 citations

Review Aug 2026

Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair

This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.

Wen-Qiang Luo, J. Keung, Xiaoyu Shi et al. · 1 citation