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FM4OR: When Foundation Models Meet Combinatorial Optimization and Reasoning — A Cross-Disciplinary Research Framework from Sun Yat-sen University

The FM4OR project, open-sourced by the OLLab team at Sun Yat-sen University, systematically reviews the latest advances of foundation models in combinatorial optimization and reasoning tasks, covering core application scenarios such as vehicle routing planning and knowledge graph reasoning, and provides researchers with a complete technical roadmap reference.

基础模型组合优化推理车辆路径规划知识图谱预训练中山大学NeurIPSICLRACL
Published 2026-05-04 09:14Recent activity 2026-05-04 09:18Estimated read 4 min
FM4OR: When Foundation Models Meet Combinatorial Optimization and Reasoning — A Cross-Disciplinary Research Framework from Sun Yat-sen University
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Section 01

FM4OR Project Introduction: A Cross-Research Framework of Foundation Models with Combinatorial Optimization and Reasoning

The FM4OR (Foundation Models for Optimization and Reasoning) project, open-sourced by the OLLab team at Sun Yat-sen University, systematically reviews the latest advances of foundation models in combinatorial optimization and reasoning tasks, covering core scenarios such as vehicle routing planning and knowledge graph reasoning. It integrates findings from top conferences like NeurIPS, ICLR, and ACL, providing researchers with a complete technical roadmap reference.

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Section 02

Background: Why Do Optimization and Reasoning Fields Need Foundation Models?

Combinatorial optimization problems (e.g., Traveling Salesman Problem, vehicle routing planning) rely on traditional algorithms but struggle with large-scale dynamic scenarios; large models have strong reasoning capabilities but are prone to hallucinations. FM4OR targets this cross-disciplinary area, integrating top conference findings to provide a panoramic technical framework.

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Section 03

Core Methods of FM4OR: Three Modules Covering Theory to Application

Panoramic Introduction to Foundation Models

Review applications in NLP, CV, and graph learning fields, leading to combinatorial optimization and graph reasoning scenarios.

Combinatorial Optimization Methods

  • Cross-task expert combination model (ICLR 2026)
  • Large model-enabled collaborative evolution fusion
  • Hypergraph Transformer pre-training
  • OPTFM (NeurIPS 2025): Multi-view Graph Transformer

Reasoning Enhancement

  • Democratized Reasoning (EMNLP 2023): Small models approach the performance of large models
  • Adaptive Solver (Information Processing and Management 2025)
  • Relationship Chain Exploration (ACL 2026 Findings)
  • Strategy-guided Planning (ACL 2026 Findings)
  • G1 (NeurIPS 2025), G-Reasoner (ICLR 2026): Reinforcement learning-based graph reasoning frameworks
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Section 04

Technical Highlights: Methodological Innovation Insights

  • Integrated pre-training and fine-tuning (Science China: Mathematics 2024)
  • Bridge between natural language and structured problems (ACL 2026 Findings benchmark)
  • Multi-agent collaboration (ICML 2024 Foundation Agents)
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Section 05

Practical Application Prospects: Value Conversion Across Multiple Domains

  • Logistics supply chain: Improve routing planning efficiency
  • Intelligent decision-making: Optimize recommendation and scheduling
  • Scientific computing: Support material design and drug discovery
  • AI system optimization: Accelerate reasoning and automate prompt engineering
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Section 06

Summary and Outlook: Research Value of FM4OR

FM4OR provides a roadmap for this field, serving as an entry point for researchers and a reference for engineers to solve problems. With the evolution of foundation models, AI is expected to achieve a qualitative leap in complex decision optimization tasks.

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