研究人员开发了一个新的学习理论框架来分析人工智能模型中的思维链（CoT）推理。该框架将与CoT相关的风险分解为两个组成部分：从最优推理路径获得的收益和沿错误路径累积错误所产生的成本。该分析表明，CoT的有效性高度依赖于其组件的稳定性，并确定了有界、线性、指数误差增长的特定条件。 AI
影响 为理解和提高人工智能模型中复杂推理的可靠性提供了理论基础。
排序理由 该集群包含一篇学术论文，详细介绍了分析人工智能推理技术的理论框架。
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arXiv:2605.24960v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) faithfulness, i.e., whether CoTs genuinely reflect large language models' (LLM) underlying behavior, is typically evaluated under two disjoint paradigms: contextual faithfulness, measured by perturbing the i…
arXiv:2605.24286v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning is useful for monitoring language models only when the reasoning trace faithfully reflects the computation that produces the final answer. However, models can rely on prompt-to-answer shortcuts tha…
We develop a learning-theoretic framework for understanding Chain of Thought (CoT). We model CoT as the interaction between an answer map and a chain rule that generates intermediate questions autoregressively, and define the reasoning risk of a hypothesis under this interaction.…
We develop a learning-theoretic framework for understanding Chain of Thought (CoT). We model CoT as the interaction between an answer map and a chain rule that generates intermediate questions autoregressively, and define the reasoning risk of a hypothesis under this interaction.…