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On the Diagram of Thought

Abstract

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 conventional approaches that represent reasoning as linear chains or tree structures, DoT organizes propositions, critiques, refinements, and verifications into a unified DAG, enabling the exploration of complex reasoning pathways while preserving logical consistency. In this framework, each node encapsulates a proposition at various stages of evaluation, thereby facilitating iterative self-improvement through detailed natural language feedback. By leveraging auto-regressive next-token prediction augmented with role-specific tokens, DoT seamlessly transitions between generating ideas and engaging in critical evaluation, offering richer, context-aware feedback than binary signals. Moreover, we establish a rigorous mathematical foundation for DoT through Topos Theory, ensuring soundness and consistency in the reasoning process. This integrated approach not only simplifies both training and inference by eliminating the need for multiple models or external control mechanisms but also provides a principled framework for the design of next-generation reasoning-specialized models.

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@article{zhang2025_2409.10038,
  title={ On the Diagram of Thought },
  author={ Yifan Zhang and Yang Yuan and Andrew Chi-Chih Yao },
  journal={arXiv preprint arXiv:2409.10038},
  year={ 2025 }
}
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