arXiv:2509.04027v3 Announce Type: replace-cross Abstract: Test-time scaling, primarily manifested through multi-step Chain-of-Thought (CoT) reasoning via Reinforcement Learning (RL), has emerged as a pivotal paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). However, a significant theoretical gap persists: traditional token-level analysis fails to capture the macroscopic dynamics of reasoning-level scaling. To address this, we introduce CoT-Space, a novel theoretical framework that recasts the reasoning process from a discrete token-prediction task to an optimiz
The paper introduces a theoretical framework for internal 'slow-thinking' in LLMs at a time when 'fast thinking' methods like CoT are prevalent, indicating a maturation in AI reasoning research.
This development could lead to significantly more robust and complex reasoning capabilities in AI, moving beyond simple token prediction to a deeper 'thought process' within models.
The focus shifts from merely scaling token-level predictions to developing theoretical frameworks that enable explicit, multi-step internal reasoning within AI models.
- · AI researchers
- · Developers of AI agents
- · Industries requiring complex decision-making
- · AI models relying solely on shallow, token-level scaling
Further acceleration in the development of sophisticated AI agents capable of multi-step reasoning.
Increased application of AI in domains requiring explainable and verifiable decision processes.
Potential for AI systems to independently discover novel solutions by simulating internal 'thought experiments'.
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Read at arXiv cs.CL