Author

Xing Hu

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Open access Oct 2026

SWE-PDB: Teaching LLMs to Leverage Debugging Tools via Agentic Training

Interaction with debugging tools enables large language models (LLMs) to reason over concrete runtime states, rather than relying solely on static analysis of source code. Specifically, with the help of a debugger, an LLM-based agent can observe actual program execution by inspecting intermediate variable states and st...

Jia-Xin-Zhang-Song-Yan Liu, Xing Hu, Xin Xia · Proceedings of the ACM on So... · 0 citations

Open access Oct 2026

SEER: Self-Enhancing Chain-of-Thought Compression for Reasoning Models

Chain-of-Thought (CoT) prompting can substantially improve the reasoning ability of large language models (LLMs), but it often comes with high inference cost due to long and poorly controlled reasoning traces. This overhead is particularly problematic in software engineering tasks (e.g., code generation), where both la...

Ke-Rui Huang, Shu-Han Liu, Xing Hu et al. · Proceedings of the ACM on So... · 0 citations