Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
LLMs often exhibit Aha moments during reasoning, such as apparent self-correction following tokens like "Wait," yet their underlying mechanisms remain unclear. We introduce an information-theoretic framework that decomposes reasoning into procedural information and epistemic verbalization - the explicit externalization of uncertainty that supports downstream control actions. We show that purely procedural reasoning can become informationally stagnant, whereas epistemic verbalization enables continued information acquisition and is critical for achieving information sufficiency. Empirical results demonstrate that strong reasoning performance is driven by uncertainty externalization rather than specific surface tokens. Our framework unifies prior findings on Aha moments and post-training experiments, and offers insights for future reasoning model design.
69b8f3f16b3f36c249fbc533
Jeonghye Kim
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2026-03-17T08:19:07.479Z
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JeonghyeKim
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user
beanie00
69b8f3f16b3f36c249fbc534
Xufang Luo
69b8f3f16b3f36c249fbc535
Minbeom Kim
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2026-03-17T08:19:05.320Z
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minbeomkim
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user
mbkim
69b8f3f16b3f36c249fbc536
Sangmook Lee
69b8f3f16b3f36c249fbc537
Dongsheng Li
69b8f3f16b3f36c249fbc538
Yuqing Yang
Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
LLMs often exhibit Aha moments during reasoning, such as apparent self-correction following tokens like "Wait," yet their underlying mechanisms remain unclear. We introduce an information-theoretic framework that decomposes reasoning into procedural information and epistemic verbalization - the explicit externalization of uncertainty that supports downstream control actions. We show that purely procedural reasoning can become informationally stagnant, whereas epistemic verbalization enables continued information acquisition and is critical for achieving information sufficiency. Empirical results demonstrate that strong reasoning performance is driven by uncertainty externalization rather than specific surface tokens. Our framework unifies prior findings on Aha moments and post-training experiments, and offers insights for future reasoning model design.