让大模型像人一样一步步逻辑推理，不烧资源还更准。
From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models
- 把推理拆成原子级步骤，动态规划思考路径。
- 在语言逻辑谜题上表现优异，计算量却远低于传统方法。
- 适合需要深度推理的复杂任务，如数学证明、法律分析。
大语言模型虽有显著进展，但在逻辑性“慢思考”推理方面仍面临关键挑战。现有推理扩展范式存在两大瓶颈：思维流碎片化导致逻辑不连贯，以及搜索空间维度增加带来的计算成本激增。为此，我们提出原子推理器（Atomic Reasoner, AR），一种通过原子级操作实现细粒度推理的认知推理策略。AR将推理过程分解为原子认知单元，利用认知路由机制动态构建推理表示并协调推理路径。该系统化方法实现了分步、结构化的认知，确保逻辑连贯性的同时大幅降低认知负担，有效模拟人类深度思考的认知模式。大量实验表明，AR在无需穷尽解空间搜索的前提下，展现出卓越的推理能力，尤其在语言逻辑谜题上表现突出。这些结果证实了AR在提升大模型稳健、长序列逻辑推理与审慎判断能力方面的有效性。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have shown remarkable progress, yet their capacity for logical ``slow-thinking'' reasoning persists as a critical research frontier. Current inference scaling paradigms suffer from two fundamental constraints: fragmented thought flows compromising logical coherence, and intensively computational complexity that escalates with search space dimensions. To overcome these limitations, we present \textbf{Atomic Reasoner} (\textbf{AR}), a cognitive inference strategy that enables fine-grained reasoning through systematic atomic-level operations. AR decomposes the reasoning process into atomic cognitive units, employing a cognitive routing mechanism to dynamically construct reasoning representations and orchestrate inference pathways. This systematic methodology implements stepwise, structured cognition, which ensures logical coherence while significantly reducing cognitive load, effectively simulating the cognitive patterns observed in human deep thinking processes. Extensive experimental results demonstrate AR's superior reasoning capabilities without the computational burden of exhaustive solution searches, particularly excelling in linguistic logic puzzles. These findings substantiate AR's effectiveness in enhancing LLMs' capacity for robust, long-sequence logical reasoning and deliberation.
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