AINeutralarXiv – CS AI · Mar 176/10
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Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
Researchers developed an information-theoretic framework to explain 'Aha moments' in large language models during reasoning tasks. The study reveals that strong reasoning performance stems from uncertainty externalization rather than specific tokens, decomposing LLM reasoning into procedural information and epistemic verbalization.