Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models 事件

Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models arXiv:2604.27251v2 Announce Type: replace Abstract: Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicited via Chain-of-Thought (CoT) practices. However, whether fundamental reasoning patterns, such as induction, deduction, and abduction, can be decoupled from specific problem instances remains a critical ch