mlsanityData to 14 Oct 2026

Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models

arXiv 2305.16582 ↗First posted 26 May 2023cs.CLcited by 52

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT's performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (ScienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT on the ScienceQA test set.

Key facts. Cited by 52 papers in the map (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 39 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

10 of 39 references.

Closest ancestors · 4

Same direction, newest first

  1. 2022Automatic Chain of Thought Prompting in Large Language Models7 Oct
  2. 2022Large Language Models are Zero-Shot Reasoners24 May
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 6

  1. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  2. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  3. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,296
  4. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 752
  5. 2023Language Is Not All You Need: Aligning Perception with Language Modelscited by 543
  6. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 356

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 14; 2025: 25; 2026: 8.

52 papers.

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  1. 2025From System 1 to System 2: A Survey of Reasoning Large Language Modelscited by 320
  2. 2025Multimodal Chain-of-Thought Reasoning: A Comprehensive Surveycited by 182
  3. 2025Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Modelscited by 90
  4. 2023Thread of Thought Unraveling Chaotic Contextscited by 72
  5. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 66
  6. 2025From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulationcited by 35
  7. 2024How Likely Do LLMs with CoT Mimic Human Reasoning?cited by 34
  8. 2023Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoningcited by 30
  9. 2024TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysiscited by 29
  10. 2026Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agentscited by 23
  11. 2023Graph Meets LLMs: Towards Large Graph Modelscited by 20
  12. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
38 more
  1. 2025OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking Systemcited by 18
  2. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  3. 2024Causal Reasoning in Large Language Models: A Knowledge Graph Approachcited by 9
  4. 2023Towards A Unified View of Answer Calibration for Multi-Step Reasoningcited by 8
  5. 2023Conditions for Length Generalization in Learning Reasoning Skillscited by 7
  6. 2024CSCE: Boosting LLM Reasoning by Simultaneous Enhancing of Causal Significance and Consistencycited by 7
  7. 2025DSADF: Thinking Fast and Slow for Decision Makingcited by 7
  8. 2026Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalizationcited by 6
  9. 2026From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AIcited by 6
  10. 2024CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMscited by 5
  11. 2025ReFineVLA: Reasoning-Aware Teacher-Guided Transfer Fine-Tuningcited by 5
  12. 2025Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Modelscited by 5
  13. 2025NeSyPr: Neurosymbolic Proceduralization For Efficient Embodied Reasoningcited by 5
  14. 2024From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Junglecited by 4
  15. 2024Reasoning Graph Enhanced Exemplars Retrieval for In-Context Learningcited by 3
  16. 2024Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Processcited by 3
  17. 2024MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLMcited by 3
  18. 2024GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Modelscited by 3
  19. 2025Weak-to-Strong Generalization with Failure Trajectories: A Tree-based Approach to Elicit Optimal Policy in Strong Modelscited by 3
  20. 2025AMELIA: A Family of Multi-task End-to-end Language Models for Argumentationcited by 3
  21. 2025Typed Chain-of-Thought: A Curry-Howard Framework for Verifying LLM Reasoningcited by 3
  22. 2026How Well Do Multimodal Models Reason on ECG Signals?cited by 3
  23. 2024LLMs as Method Actors: A Model for Prompt Engineering and Architecturecited by 2
  24. 2025Weaker LLMs' Opinions Also Matter: Mixture of Opinions Enhances LLM's Mathematical Reasoningcited by 2
  25. 2025How Deep is Love in LLMs' Hearts? Exploring Semantic Size in Human-like Cognitioncited by 2
  26. 2025Empowering Multimodal LLMs with External Tools: A Comprehensive Surveycited by 2
  27. 2025Improving Factuality in LLMs via Inference-Time Knowledge Graph Constructioncited by 2
  28. 2025MDToC: Metacognitive Dynamic Tree of Concepts for Boosting Mathematical Problem-Solving of Large Language Modelscited by 2
  29. 2025Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Modelscited by 1
  30. 2025Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Modelscited by 1
  31. 2025CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Taskscited by 1
  32. 2025Multi-chain Graph Refinement and Selection for Reliable Reasoning in Large Language Modelscited by 1
  33. 2026ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuningcited by 1
  34. 2026What makes prompts a graph: necessary and sufficient conditions for prompt graph engineeringcited by 1
  35. 2024Seeing the Undefined: Chain-of-Action for Generative Semantic Labelscited by 0
  36. 2025Graph-of-Causal Evolution: Challenging Chain-of-Model for Reasoningcited by 0
  37. 2025Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoningcited by 0
  38. 2025Knots: A Large-Scale Multi-Agent Enhanced Expert-Annotated Dataset and LLM Prompt Optimization for NOTAM Semantic Parsingcited by 0

50 most cited of 52 All 52 (JSON)

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  1. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Models
  2. 2023Why Can Large Language Models Generate Correct Chain-of-Thoughts?
  3. 2025Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
  4. 2026Structural Chain-of-Thought: Reasoning with Executable Computational Graphs
  5. 2024Chain-of-Thought in Large Language Models: Decoding, Projection, and Activation
  6. 2024Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought
  7. 2023ThoughtSource: A central hub for large language model reasoning data
  8. 2025Chain or tree? Re-evaluating complex reasoning from the perspective of a matrix of thought