mlsanityData to 7 Oct 2026

Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization

arXiv 2408.12214 ↗First posted 22 Aug 2024cs.AIcited by 8

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Abstract (arXiv)

To advance capabilities of large language models (LLMs) in solving combinatorial optimization problems (COPs), this paper presents the Language-based Neural COP Solver (LNCS), a novel framework that is unified for the end-to-end resolution of diverse text-attributed COPs. LNCS leverages LLMs to encode problem instances into a unified semantic space, and integrates their embeddings with a Transformer-based solution generator to produce high-quality solutions. By training the solution generator with conflict-free multi-task reinforcement learning, LNCS effectively enhances LLM performance in tackling COPs of varying types and sizes, achieving state-of-the-art results across diverse problems. Extensive experiments validate the effectiveness and generalizability of the LNCS, highlighting its potential as a unified and practical framework for real-world COP applications.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 44 of its 53 references that are in the map. First posted on arXiv 22 Aug 2024.

Builds on

44 of 53 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 4.

8 papers.

  1. 2025Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Designcited by 100
  2. 2024A Systematic Survey on Large Language Models for Algorithm Designcited by 65
  3. 2025Large Language Models as End-to-end Combinatorial Optimization Solverscited by 27
  4. 2025Large Language Models in Operations Research: Methods, Applications, and Challengescited by 4
  5. 2026DRAGON: LLM-Driven Decomposition and Reconstruction Agents for Large-Scale Combinatorial Optimizationcited by 3
  6. 2026Aligning LLMs with Graph Neural Solvers for Combinatorial Optimizationcited by 1
  7. 2026BEAM: Bi-level Memory-adaptive Algorithmic Evolution for LLM-Powered Heuristic Designcited by 1
  8. 2026Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Predictioncited by 1