arXiv:2605.22873v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has become the default strategy for enhancing LLM capabilities, yet its application raises a fundamental question: when is explicit reasoning actually beneficial? Empirical evidence reveals a striking paradox: CoT often provides marginal or even negative gains on factual and open-ended tasks while multiplying token consumption. In this work, we show that LLM reasoning is not a static property of tasks or models, but a \emph{dynamic decoding state} that emerges during generation. Through systematic analysis, we fin
The paper provides a dynamic systems view of LLM reasoning, a critical step as AI foundational models become more integrated and their inner workings are scrutinized for efficiency and reliability.
Understanding when and how LLMs 'reason' can lead to more efficient and explainable AI, moving beyond brute-force token consumption to targeted, beneficial reasoning strategies.
This research shifts the understanding of LLM reasoning from a static model property to a dynamic emergent state during generation, potentially leading to more sophisticated control over AI outputs.
- · AI researchers
- · Developers of custom LLMs
- · SaaS providers leveraging advanced AI
- · Companies seeking explainable AI
- · Companies reliant on inefficient CoT reasoning
- · Foundational model providers with opaque reasoning processes
Increased research and development into dynamic reasoning control for LLMs.
Development of adaptive LLM architectures that optimize reasoning based on task and real-time generation state.
Enhanced AI agents capable of self-modifying reasoning processes, leading to more robust and less resource-intensive autonomous systems.
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Read at arXiv cs.LG