3 papers
cs.AI2026
"I May Not Have Articulated Myself Clearly": Diagnosing Dynamic Instability in LLM Reasoning at Inference Time
Jinkun Chen, Fengxiang Cheng, Sijia Han +1
Reasoning failures in large language models (LLMs) are typically measured only at the end of a generation, yet many failures manifest as a process-level breakdown: the model "loses…
cs.AI2025
Adaptive Selection of Symbolic Languages for Improving LLM Logical Reasoning
Xiangyu Wang, Haocheng Yang, Fengxiang Cheng +1
Large Language Models (LLMs) still struggle with complex logical reasoning. While previous works achieve remarkable improvements, their performance is highly dependent on the corre…
cs.AI2025
Empowering LLMs with Logical Reasoning: A Comprehensive Survey
Fengxiang Cheng, Haoxuan Li, Fenrong Liu +3
Large language models (LLMs) have achieved remarkable successes on various tasks. However, recent studies have found that there are still significant challenges to the logical reas…