How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning 事件

How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning arXiv:2605.23926v1 Announce Type: new Abstract: Reasoning-capable large language models solve hard problems by emitting long chains of thought, paying heavily in latency, GPU time, and energy. Casual inspection of their traces reveals extensive reformulation, verification, and circular self-reflection, yet how much of this deliberation is actually necessary has never been measured at scale or explained from f