GraphThought: Graph Combinatorial Optimization with Thought Generation

• URL: http://arxiv.org/abs/2502.11607v2
• Date: Thu, 12 Jun 2025 16:10:05 GMT
• Title: GraphThought: Graph Combinatorial Optimization with Thought Generation
• Authors: Zixiao Huang, Lifeng Guo, Wenhao Li, Junjie Sheng, Chuyun Shen, Haosheng Chen, Bo Jin, Changhong Lu, Xiangfeng Wang,
• Abstract summary: Graph optimization (GCO) problems are central to domains like logistics and bioinformatics.<n>We first formalize the Optimal Thoughts Design (OTD) problem, which provides a structured guidance for producing high-quality intermediate reasoning steps.<n>We introduce GraphThought, a novel framework that generates effective reasoning sequences through either-guided forward search or solver-aligned backward reasoning.
• Score: 16.457287060520454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph combinatorial optimization (GCO) problems are central to domains like logistics and bioinformatics. While traditional solvers dominate, large language models (LLMs) offer new possibilities for structured reasoning, yet struggle with complex GCO tasks requiring rigorous combinatorial analysis and multi-step deduction, often producing hallucinated steps. We first formalize the Optimal Thoughts Design (OTD) problem, which provides a structured guidance for producing high-quality intermediate reasoning steps. Building on this formulation, we introduce GraphThought, a novel framework that generates effective reasoning sequences through either heuristic-guided forward search or solver-aligned backward reasoning. By fine-tuning LLMs on these structured thought sequences, we develop Llama-GT, an 8B-parameter model that achieves state-of-the-art performance on the GraphArena benchmark, outperforming significantly larger models like DeepSeek-V3. Our results demonstrate that when scaffolded with structured reasoning priors, principled thought generation can significantly enhance LLM performance on GCO tasks without requiring increased model scale.

Related papers

• GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning [33.57411612551111]
  We propose GraphRAG-R1, an adaptive GraphRAG framework by training LLMs with process-constrained outcome-based reinforcement learning (RL)<n>Our method can decompose complex problems, autonomously invoke retrieval tools, and perform effective reasoning.<n>Our framework can be flexibly integrated with various existing retrieval methods, consistently delivering performance improvements.
  arXiv  Detail & Related papers  (2025-07-31T14:11:16Z)
• Visualizing Thought: Conceptual Diagrams Enable Robust Planning in LMMs [57.66267515456075]
  Large Language Models (LLMs) and Large Multimodal Models (LMMs) predominantly reason through textual representations.<n>We propose a zero-shot fully automatic framework that enables LMMs to reason through multiple chains of self-generated conceptual diagrams.
  arXiv  Detail & Related papers  (2025-03-14T18:27:02Z)
• Rewarding Graph Reasoning Process makes LLMs more Generalized Reasoners [30.195361623027313]
  Process Reward Models (PRMs) have demonstrated exceptional promise in enhancing reasoning by providing step-wise feedback. We introduce GraphSILO, the largest dataset for graph reasoning problems with fine-grained step-wise labels. We train GraphPRM, the first PRM designed for graph reasoning problems, and evaluate its effectiveness in two key settings.
  arXiv  Detail & Related papers  (2025-03-02T10:39:40Z)
• Adaptive Graph of Thoughts: Test-Time Adaptive Reasoning Unifying Chain, Tree, and Graph Structures [0.0]
  We introduce Adaptive Graph of Thoughts (AGoT), a dynamic, graph-based inference framework.<n>AGoT enhances Large Language Models (LLMs) reasoning solely at test time.<n>We validate our approach on diverse benchmarks spanning multi-hop retrieval, scientific reasoning, and mathematical problem-solving.
  arXiv  Detail & Related papers  (2025-02-07T16:54:19Z)
• BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks.<n>We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model.<n>We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
  arXiv  Detail & Related papers  (2025-01-31T02:39:07Z)
• AutoG: Towards automatic graph construction from tabular data [60.877867570524884]
  We introduce a set of datasets to formalize and evaluate graph construction methods.<n>We propose an LLM-based solution, AutoG, which automatically generates high-quality graph schemas without human intervention.
  arXiv  Detail & Related papers  (2025-01-25T17:31:56Z)
• LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration [17.514586423233872]
  We propose LEGO-GraphRAG, a modular framework that enables fine-grained decomposition of the GraphRAG workflow.<n>Our framework facilitates comprehensive empirical studies of GraphRAG on large-scale real-world graphs and diverse query sets.
  arXiv  Detail & Related papers  (2024-11-06T15:32:28Z)
• Assessing and Enhancing Graph Neural Networks for Combinatorial Optimization: Novel Approaches and Application in Maximum Independent Set Problems [0.0]
  Graph Neural Networks (GNNs) show promise for researchers in solving Combinatorial optimization (CO) problems. This study investigates the effectiveness of GNNs in solving the maximum independent set (MIS) problem.
  arXiv  Detail & Related papers  (2024-11-06T09:12:31Z)
• GCoder: Improving Large Language Model for Generalized Graph Problem Solving [38.9131866084555]
  Large Language Models (LLMs) have demonstrated strong reasoning abilities, making them suitable for complex tasks such as graph computation. We introduce GCoder, a code-based LLM designed to enhance problem-solving in generalized graph problems. Our method involves constructing an extensive training dataset, GraphWild, featuring diverse graph formats and algorithms.
  arXiv  Detail & Related papers  (2024-10-24T18:40:36Z)
• Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents [27.4884498301785]
  We introduce GraphAgent-Reasoner, a fine-tuning-free framework for explicit and precise graph reasoning. Inspired by distributed graph computation theory, our framework decomposes graph problems into smaller, node-centric tasks that are distributed among multiple agents. Our framework demonstrates the capability to handle real-world graph reasoning applications such as webpage importance analysis.
  arXiv  Detail & Related papers  (2024-10-07T15:34:14Z)
• How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension [53.6373473053431]
  This work introduces a benchmark to assess large language models' capabilities in graph pattern tasks. We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions. Our benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
  arXiv  Detail & Related papers  (2024-10-04T04:48:33Z)
• GLBench: A Comprehensive Benchmark for Graph with Large Language Models [41.89444363336435]
  We introduce GLBench, the first comprehensive benchmark for evaluating GraphLLM methods in both supervised and zero-shot scenarios. GLBench provides a fair and thorough evaluation of different categories of GraphLLM methods, along with traditional baselines such as graph neural networks.
  arXiv  Detail & Related papers  (2024-07-10T08:20:47Z)
• MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time [51.5039731721706]
  MindStar is a purely inference-based searching method for large language models. It formulates reasoning tasks as searching problems and proposes two search ideas to identify the optimal reasoning paths. It significantly enhances the reasoning abilities of open-source models, such as Llama-2-13B and Mistral-7B, and achieves comparable performance to GPT-3.5 and Grok-1.
  arXiv  Detail & Related papers  (2024-05-25T15:07:33Z)
• PanGu-$\pi$: Enhancing Language Model Architectures via Nonlinearity Compensation [97.78045712375047]
  We present a new efficient model architecture for large language models (LLMs) We show that PanGu-$pi$-7B can achieve a comparable performance to that of benchmarks with about 10% inference speed-up. In addition, we have deployed PanGu-$pi$-7B in the high-value domains of finance and law, developing an LLM named YunShan for practical application.
  arXiv  Detail & Related papers  (2023-12-27T11:49:24Z)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks [52.566735716983956]
  We propose a graph gradual pruning framework termed CGP to dynamically prune GNNs. Unlike LTH-based methods, the proposed CGP approach requires no re-training, which significantly reduces the computation costs. Our proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of existing methods.
  arXiv  Detail & Related papers  (2022-07-18T14:23:31Z)
• Towards Unsupervised Deep Graph Structure Learning [67.58720734177325]
  We propose an unsupervised graph structure learning paradigm, where the learned graph topology is optimized by data itself without any external guidance. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph.
  arXiv  Detail & Related papers  (2022-01-17T11:57:29Z)
• Multi-task Learning of Order-Consistent Causal Graphs [59.9575145128345]
  We consider the problem of discovering $K related Gaussian acyclic graphs (DAGs) Under multi-task learning setting, we propose a $l_1/l$-regularized maximum likelihood estimator (MLE) for learning $K$ linear structural equation models. We theoretically show that the joint estimator, by leveraging data across related tasks, can achieve a better sample complexity for recovering the causal order.
  arXiv  Detail & Related papers  (2021-11-03T22:10:18Z)
• DAGs with No Curl: An Efficient DAG Structure Learning Approach [62.885572432958504]
  Recently directed acyclic graph (DAG) structure learning is formulated as a constrained continuous optimization problem with continuous acyclicity constraints. We propose a novel learning framework to model and learn the weighted adjacency matrices in the DAG space directly. We show that our method provides comparable accuracy but better efficiency than baseline DAG structure learning methods on both linear and generalized structural equation models.
  arXiv  Detail & Related papers  (2021-06-14T07:11:36Z)
• A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs [91.07247251502564]
  We propose a hybrid approach to combine the best of the two worlds, in which a bi-level framework is developed with an upper-level learning method to optimize the graph. Such a bi-level approach simplifies the learning on the original hard CO and can effectively mitigate the demand for model capacity.
  arXiv  Detail & Related papers  (2021-06-09T09:18:18Z)

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.