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How Hard Does It Think? Analyzing Step-Aware Reasoning Energy in LLM Chain-of-Thought Trajectories

Source
arxiv.org
Author
Hui Wei, Junda Wu, Sheldon Yu, Sizhe Zhou, Yizhu Jiao, Ming Zhong, Bowen Jin, Tong Yu, Shijia Pan, Jiawei Han, Julian McAuley
Date
Why it matters

Internal per-step geometry predicts when a reasoning chain has gone wrong better than output confidence does — a usable signal for deciding when to re-run, branch, or escalate a reasoning trace.

Terms in this piece · Glossary
  • chain-of-thought — Having a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.
  • alignment — The work of making AI systems actually pursue what their builders and users intend, rather than something subtly or dangerously different.
  • token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
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