Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression

ArXi:2510.08647v2 Announce Type: replace-cross Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance. Existing works focus on compressing generated CoT in reasoning, which impairs the necessary information for deriving the correct answer. In this work, we propose post-reasoning, a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for LLMs.