- A Survey on Large Language Models for Code Generation, Jiang et al.,
- LLM4SR: A Survey on Large Language Models for Scientific Research, Luo et al.,
- Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey, Gu et al.,
- MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering, Chan et al.,
- CO-Bench: Benchmarking Language Model Agents in Algorithm Search for Combinatorial Optimization, Sun et al.,
- MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research, Chen et al.,
- MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement, Nam et al.,
- AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench, Toledo et al.,
- R&D-Agent: Automating Data-Driven AI Solution Building Through LLM-Powered Automated Research, Development, and Evolution, Yang et al.,
- ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning, Liu et al.,
- AutoMind: Adaptive Knowledgeable Agent for Automated Data Science, Ou et al.,
- Illuminating LLM Coding Agents: Visual Analytics for Deeper Understanding and Enhancement, Wang et al.,
- MLZero: A Multi-Agent System for End-to-end Machine Learning Automation, Fang et al.,
- AIDE: AI-Driven Exploration in the Space of Code, Jiang et al.,
- OpenHands: An Open Platform for AI Software Developers as Generalist Agents, Wang et al.,
- ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models, Baek et al.,
- Agent Laboratory: Using LLM Agents as Research Assistants, Schmidgall et al.,
- Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance, Anonymous et al.,
- AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions, Li et al.,
- Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets, Bendinelli et al.,
- KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems, Kulibaba et al.,
- MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering, Qiang et al.,
- ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering, Liu et al.,
- ResearchCodeAgent: An LLM Multi-agent System for Automated Codification of Research Methodologies, Gandhi et al.,
