3 papers
cs.LG2026
R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
Aijia Cheng, Kailong Wang, Ling Shi +1
Function calling empowers large language models (LLMs) to interface with external tools, yet existing RL-based approaches suffer from misalignment between reasoning processes and t…
cs.SE2025
Beyond Correctness: Exposing LLM-generated Logical Flaws in Reasoning via Multi-step Automated Theorem Proving
Xinyi Zheng, Ningke Li, Xiaokun Luan +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, leading to their adoption in high-stakes domains such as healthcare, law, and scientific research.…
cs.SE2025
Large Language Models are overconfident and amplify human bias
Fengfei Sun, Ningke Li, Kailong Wang +1
Large language models (LLMs) are revolutionizing every aspect of society. They are increasingly used in problem-solving tasks to substitute human assessment and reasoning. LLMs are…