{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/on-the-diagram-of-thought","title":"On the Diagram of Thought","arxiv_id":"2409.10038","date":"2024-09-16","proceeding":null,"authors":["Yifan Zhang","Yang Yuan","Andrew Chi-Chih Yao"],"abstract":"Current large language models (LLMs) demonstrate impressive capabilities but struggle with complex, multi-step reasoning tasks. Existing methods often tackle this by requiring external control mechanisms or multi-model orchestration, which introduces system complexity and typically lacks formal guarantees of reasoning soundness. We introduce the Diagram of Thought (DoT), a framework wherein a single auto-regressive LLM internally constructs and navigates a Directed Acyclic Graph (DAG). This DAG represents the iterative reasoning process, encompassing steps like proposing ideas, critiquing them, refining based on feedback, and synthesizing conclusions. This self-orchestrated, self-contained process is guided by learned role-specific tokens (e.g., <proposer>, <critic>, <summarizer>) embedded within the standard generation loop, thereby eliminating external dependencies. Crucially, we establish a rigorous mathematical foundation for DoT using Topos Theory. We formalize the reasoning DAG as a diagram within a suitable topos and prove that the final synthesis step, aggregating validated information, corresponds semantically to computing the colimit of the relevant sub-diagram. This formalization provides theoretical guarantees concerning the logical consistency and robustness of the synthesized outcome. DoT thus offers a unified, self-contained, interpretable, efficient, and formally grounded approach designed to significantly advance the complex reasoning capabilities of LLMs.","url_abs":"https://arxiv.org/abs/2409.10038v3","url_pdf":"https://arxiv.org/pdf/2409.10038v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"on-the-diagram-of-thought","repo_url":"https://github.com/diagram-of-thought/diagram-of-thought","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2409.10038","atlas_url":"https://app.syntology.ai/?focus=2409.10038","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}