AI RESEARCH
From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs
arXiv CS.CL
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ArXi:2601.03682v2 Announce Type: replace Recent studies reveal that large language models (LLMs) exhibit limited logical reasoning abilities in mathematical problem-solving, instead often relying on pattern-matching and memorization. We systematically analyze this limitation, focusing on logical relationship understanding, which is a core capability underlying genuine logical reasoning, and reveal that errors related to this capability account for over 90\% of incorrect predictions, with Chain-of-Thought Supervised Fine-Tuning (CoT-SFT) failing to substantially reduce these errors.