LLM4AlgorithmDesign
A Collection on Large Language Models for Optimization
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Decision brief
LLM4AlgorithmDesign is a valuable resource for researchers and practitioners focusing on the intersection of large language models with algorithm design and optimization.
Good fit when
- - You are a researcher who needs access to a comprehensive set of references and papers focused specifically on using large language models (LLMs) in algorithm design and optimization.
- - Your team is involved in academic projects or competitions where an overview of state-of-the-art techniques for LLMs applied to algorithm design is required, as it includes categorized research and匍
Avoid when
- - If you require a hands-on development framework but without the specific focus on optimizing algorithms through large language models.
- - You are looking for a platform with active development contributions from users. LLM4AlgorithmDesign primarily serves as a repository of references, which means its primary utility is in referencing
- Pricing:
- freemium - As the repository's license information and language are unknown, assume it to be free but use only for research purpose
- Requirements:
- - The main requirement is an interest in large Language Models (LLMs) in algorithm design and optimization.; - Familiarity with Python may be an advantage, considering the mentioned LLM4AD platform is Python-based.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
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- Not a fork · Personal account
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Install
git clone https://github.com/FeiLiu36/LLM4AlgorithmDesignSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository contains a curated collection of references and papers focused on the application of Large Language Models (LLMs) in algorithm design and optimization.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 6, 2026)
| [LLM4AD](https://github.com/Optima-CityU/LLM4AD) | Open-source Python-based Platform leveraging Large Language Models (LLMs) for Automatic AlgorithmSource link
Tags
README
LLM4AlgorithmDesign
Collection on Algorithm Design with Large Language Models.
🔥 Applying Large language models (LLMs) for algorithm design (AD) is an emerging research area. This is a collection of references and papers of LLM4AD (with focus on optimization algorithms). The Papers are sorted by time (first publicly available).
For more details, please see our survey paper:
- A Systematic Survey on Large Language Models for Algorithm Design (ACM Computing Surveys, 2025)
@article{liu2025systematic,
author = {Liu, Fei and Yao, Yiming and Guo, Ping and Yang, Zhiyuan and Lin, Xi and Zhao, Zhe and Tong, Xialiang and Mao, Kun and Lu, Zhichao and Wang, Zhenkun and Yuan, Mingxuan and Zhang, Qingfu},
title = {A Systematic Survey on Large Language Models for Algorithm Design},
year = {2025},
journal = {ACM Computing Surveys}
}
Video Introductions and Slides:
Any suggestions and pull requests are welcomed!
It is far from a comprehensive list. If you want to update the list:
- Fork, Add, and Merge
- Report an issue
- Contact Fei Liu (fliu36-c@my.cityu.edu.hk)
The sharing principle of these references here is for research. If any authors do not want their paper to be listed here, please feel free to contact us.
Overview
- Platform
- Course
- Tutorial
- Competition
- Special Issues
- Research Papers in Four Paradigms
- Research Papers
- Related Collections
Platform
| Project | Description |
|---|---|
| LLM4AD | Open-source Python-based Platform leveraging Large Language Models (LLMs) for Automatic Algorithm Design (AD) with 100+ tasks and 10+ methods |
| BLADE | Benchmarking LLM-driven Automated Design and Evolution of Iterative Optimization Heuristics |
| EASE | Effortless Algorithmic Solution Evolution is a framework that leverages Large Language Models (LLMs) to generate solutions (algorithms, text, images, etc.) based on user-defined parameters. It provides a flexible and adaptive approach to automated problem-solving. |
Course
| Course | Description |
|---|---|
| 2024 Fall, LLM Agents | LLM basics and LLM for agents |
Tutorial&Workshop
|
For agents
This page has a .md twin and JSON over the API.