On the Diagram of Thought

• URL: http://arxiv.org/abs/2409.10038v3
• Date: Sun, 30 Mar 2025 23:31:29 GMT
• Title: On the Diagram of Thought
• Authors: Yifan Zhang, Yang Yuan, Andrew Chi-Chih Yao,
• Abstract summary: Current large language models (LLMs) demonstrate impressive capabilities but struggle with complex, multi-step reasoning tasks.<n>We introduce the Diagram of Thought (DoT) as a framework wherein a single auto-regressive LLM internally constructs and navigates a Directed Acyclic Graph (DAG)<n>We formalize the reasoning DAG as a diagram within a suitable topos and prove that the final step, aggregating validated information, corresponds semantically to computing the colimit of the relevant sub-diagram.
• Score: 12.304069891580658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution [59.39066657300045]
  Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps. We propose Syzygy of Thoughts (SoT)-a novel framework that extends CoT by introducing auxiliary, interrelated reasoning paths. SoT captures deeper logical dependencies, enabling more robust and structured problem-solving.
  arXiv  Detail & Related papers  (2025-04-13T13:35:41Z)
• Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation [24.081573908824353]
  First-order logic (FOL) reasoning is pivotal for intelligent systems. Existing benchmarks often rely on extensive human annotation or handcrafted templates. We propose a novel framework called ProverGen that synergizes the generative strengths of Large Language Models with the rigor and precision of symbolic provers.
  arXiv  Detail & Related papers  (2025-02-10T15:31:54Z)
• BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model. We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
  arXiv  Detail & Related papers  (2025-01-31T02:39:07Z)
• RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner [2.779063752888881]
  Self-taught reasoner (STaR) framework uses reinforcement learning to automatically generate reasoning steps. STaR and its variants have demonstrated empirical success, but a theoretical foundation explaining these improvements is lacking. This work provides a theoretical framework for understanding the effectiveness of reinforcement learning on CoT reasoning and STaR.
  arXiv  Detail & Related papers  (2024-10-31T13:17:53Z)
• Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up [9.42385235462794]
  Large language models (LLMs) have shown remarkable performance in reasoning tasks but face limitations in mathematical and complex logical reasoning. We propose Reversal of Thought (RoT), a novel framework aimed at enhancing the logical reasoning abilities of LLMs. RoT utilizes a Preference-Guided Reverse Reasoning warm-up strategy, which integrates logical symbols for pseudocode planning.
  arXiv  Detail & Related papers  (2024-10-16T07:44:28Z)
• Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning [1.3003982724617653]
  Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge.
  arXiv  Detail & Related papers  (2024-09-25T18:35:45Z)
• Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought [31.964412924094656]
  Large language models (LLMs) have shown exceptional performance as general-purpose assistants. We introduce a novel learning framework, THOUGHT-LIKE-PRO, to facilitate learning and generalization across diverse reasoning tasks. Our empirical findings indicate that our proposed approach substantially enhances the reasoning abilities of LLMs.
  arXiv  Detail & Related papers  (2024-07-18T18:52:10Z)
• The Buffer Mechanism for Multi-Step Information Reasoning in Language Models [52.77133661679439]
  Investigating internal reasoning mechanisms of large language models can help us design better model architectures and training strategies. In this study, we constructed a symbolic dataset to investigate the mechanisms by which Transformer models employ vertical thinking strategy. We proposed a random matrix-based algorithm to enhance the model's reasoning ability, resulting in a 75% reduction in the training time required for the GPT-2 model.
  arXiv  Detail & Related papers  (2024-05-24T07:41:26Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning [74.90592233107712]
  We propose a Direct-Indirect Reasoning (DIR) method, which considers Direct Reasoning (DR) and Indirect Reasoning (IR) as multiple parallel reasoning paths that are merged to derive the final answer. Our DIR method is simple yet effective and can be straightforwardly integrated with existing variants of CoT methods.
  arXiv  Detail & Related papers  (2024-02-06T03:41:12Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models [74.40196814292426]
  We propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. GoT captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. We evaluate GoT's performance on a text-only reasoning task and a multimodal reasoning task.
  arXiv  Detail & Related papers  (2023-05-26T02:15:09Z)
• Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs [67.043747188954]
  We propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs. It encodes linearized query structures and entities using pre-trained language models to find answers. We conduct experiments on two inductive logical reasoning datasets and three transductive datasets.
  arXiv  Detail & Related papers  (2023-05-23T01:25:29Z)
• Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings [61.04460792203266]
  We introduce VCoT, a novel method that leverages chain-of-thought prompting with vision-language grounding to bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks.
  arXiv  Detail & Related papers  (2023-05-03T17:58:29Z)
• Chaining Simultaneous Thoughts for Numerical Reasoning [92.2007997126144]
  numerical reasoning over text should be an essential skill of AI systems. Previous work focused on modeling the structures of equations, and has proposed various structured decoders. We propose CANTOR, a numerical reasoner that models reasoning steps using a directed acyclic graph.
  arXiv  Detail & Related papers  (2022-11-29T18:52:06Z)
• Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought [10.524051272257614]
  Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts. We present a new synthetic question-answering dataset called PrOntoQA, where each example is generated as a synthetic world model. This allows us to parse the generated chain-of-thought into symbolic proofs for formal analysis.
  arXiv  Detail & Related papers  (2022-10-03T21:34:32Z)
• Linear Temporal Logic Modulo Theories over Finite Traces (Extended Version) [72.38188258853155]
  This paper studies Linear Temporal Logic over Finite Traces (LTLf) proposition letters are replaced with first-order formulas interpreted over arbitrary theories. The resulting logic, called Satisfiability Modulo Theories (LTLfMT), is semi-decidable.
  arXiv  Detail & Related papers  (2022-04-28T17:57:33Z)
• DAReN: A Collaborative Approach Towards Reasoning And Disentangling [27.50150027974947]
  We propose an end-to-end joint representation-reasoning learning framework, which leverages a weak form of inductive bias to improve both tasks together. We accomplish this using a novel learning framework Disentangling based Abstract Reasoning Network (DAReN) based on the principles of GM-RPM.
  arXiv  Detail & Related papers  (2021-09-27T16:10:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.