思維圖譜

摘要

我們介紹了思維圖（DoT），這是一個框架，將大型語言模型（LLMs）中的迭代推理建模為在單個模型內構建有向無環圖（DAG）。與將推理表示為線性鏈或樹的傳統方法不同，DoT將命題、批評、改進和驗證組織成一個統一的DAG結構，使模型能夠探索複雜的推理路徑，同時保持邏輯一致性。圖中的每個節點對應於一個已提出、批評、改進或驗證的命題，使LLM能夠通過自然語言反饋逐步改進其推理。通過利用自回歸的下一令牌預測和角色特定的令牌，DoT促進了在提出想法和批判性評估之間的無縫過渡，提供比二元信號更豐富的反饋。此外，我們使用拓撲理論對DoT框架進行了形式化，提供了一個數學基礎，確保推理過程中的邏輯一致性和嚴謹性。這種方法增強了單個LLM內的訓練和推理過程，消除了對多個模型或外部控制機制的需求。DoT提供了一個概念框架，用於設計下一代專門用於推理的模型，強調訓練效率、強大的推理能力和理論基礎。代碼可在https://github.com/diagram-of-thought/diagram-of-thought找到。

English

We introduce Diagram of Thought (DoT), a framework that models iterative reasoning in large language models (LLMs) as the construction of a directed acyclic graph (DAG) within a single model. Unlike traditional approaches that represent reasoning as linear chains or trees, DoT organizes propositions, critiques, refinements, and verifications into a cohesive DAG structure, allowing the model to explore complex reasoning pathways while maintaining logical consistency. Each node in the diagram corresponds to a proposition that has been proposed, critiqued, refined, or verified, enabling the LLM to iteratively improve its reasoning through natural language feedback. By leveraging auto-regressive next-token prediction with role-specific tokens, DoT facilitates seamless transitions between proposing ideas and critically evaluating them, providing richer feedback than binary signals. Furthermore, we formalize the DoT framework using Topos Theory, providing a mathematical foundation that ensures logical consistency and soundness in the reasoning process. This approach enhances both the training and inference processes within a single LLM, eliminating the need for multiple models or external control mechanisms. DoT offers a conceptual framework for designing next-generation reasoning-specialized models, emphasizing training efficiency, robust reasoning capabilities, and theoretical grounding. The code is available at https://github.com/diagram-of-thought/diagram-of-thought.

On the Diagram of Thought

摘要

Summary

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