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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Recent research papers about Foundation Models for Combinatorial Optimization
| Date | Stars |
|---|---|
| 2026-07-31 | 573 |
| 2026-08-06 | 579 |
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<h1 align="center">Foundation Models for Combinatorial Optimization</h1> FM4CO contains interesting research papers (1) using **Existing Large Language Models for Combinatorial Optimization**, and (2) building **Domain Foundation Models for Combinatorial Optimization**. ---- ### LLMs for Combinatorial Optimization Most research utilizes existing FMs from language and vision domains to generate/improve solutions\* or algorithms\* (hyper-heuristic), yielding impressive results when integrated with problem-specific heuristics or general meta-heuristics. Other studies employ LLMs to investigate the interpretability\* of COP solvers, automate problem formulation*, or simplify the use of domain-specific tools through text prompts. Given the capabilities of LLMs, this area of research is likely to garner increasing interest. | Date | Paper | Link | Problem | Venue | Remark* | | :-----: | :----------------------------------------------------------: | :----------------------------------------------------------: | :-------------------: | :------------: | :--------------: | | 2023.07 | [Large Language Models for Supply Chain Optimization](https://arxiv.org/pdf/2307.03875) | [](https://github.com/microsoft/OptiGuide) | `Supply_Chain` | *arXiv* | Algorithm w. Interpretability | | 2023.09 | [Can Language Models Solve Graph Problems in Natural Language?](https://arxiv.org/pdf/2305.10037) | [](https://github.com/Arthur-Heng/NLGraph) | `Graph` | *NeurIPS 2023* | Solution | | 2023.09 | [Large Language Models as Optimizers](https://arxiv.org/pdf/2309.03409) | [](https://github.com/google-deepmind/opro) | `TSP` | *ICLR 2024* | Solution | | 2023.10 | [Chain-of-Experts: When LLMs Meet Complex Operations Research Problems](https://openreview.net/pdf?id=HobyL1B9CZ) | [](https://github.com/xzymustbexzy/Chain-of-Experts) | `MILP` | *ICLR 2024* | Formulation | | 2023.10 | [OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models](https://arxiv.org/pdf/2402.10172) | [](https://github.com/teshnizi/OptiMUS) | `MILP` | *ICML 2024* | Formulation | | 2023.10 | [AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling](https://arxiv.org/pdf/2309.13218) | [](https://github.com/pivithuruthejanamarasinghe/AI-Copilot-Data) | `JSSP` | *arXiv* | Formulation | | 2023.11 | [Large Language Models as Evolutionary Optimizers](https://arxiv.org/pdf/2310.19046) | [](https://github.com/cschen1205/LMEA) | `TSP` | *CEC 2024* | Solution | | 2023.11 | [Algorithm Evolution Using Large Language Model](https://arxiv.org/pdf/2311.15249) |     | `TSP` | *arXiv* | Algorithm | | 2023.12 | [Mathematical discoveries from program search with large language models](https://www.nature.com/articles/s41586-023-06924-6) | [](https://github.com/google-deepmind/funsearch) | `BPP` | *Nature* | Algorithm | | 2023.12 | [NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes](https://arxiv.org/pdf/2312.14890) | [
