# R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science

arXiv: https://arxiv.org/abs/2505.14738 · HTML: https://mlsanity.com/paper/2505.14738/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 20 May 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering](https://mlsanity.com/paper/2410.07095/) (2024)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation](https://mlsanity.com/paper/2310.03302/) (2023)
- [AIDE: AI-Driven Exploration in the Space of Code](https://mlsanity.com/paper/2502.13138/) (2025)
- [DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?](https://mlsanity.com/paper/2409.07703/) (2024)
- [Scaling Laws for Neural Machine Translation](https://mlsanity.com/paper/2109.07740/) (2021)
- [DiscoveryBench: Towards Data-Driven Discovery with Large Language Models](https://mlsanity.com/paper/2407.01725/) (2024)
- [KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation](https://mlsanity.com/paper/2409.13731/) (2024)
- [TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets](https://mlsanity.com/paper/2502.01506/) (2025)
- [InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation](https://mlsanity.com/paper/2407.06423/) (2024)
- [AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models](https://mlsanity.com/paper/2404.16233/) (2024)
- [MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models](https://mlsanity.com/paper/2409.15477/) (2024)
- [ContextGNN: Beyond Two-Tower Recommendation Systems](https://mlsanity.com/paper/2411.19513/) (2024)

## Built on by (most cited first, 30 of 41)

- [ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning](https://mlsanity.com/paper/2506.16499/) (2025)
- [MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery](https://mlsanity.com/paper/2606.06473/) (2026)
- [MARS: Modular Agent with Reflective Search for Automated AI Research](https://mlsanity.com/paper/2602.02660/) (2026)
- [Toward Autonomous Long-Horizon Engineering for ML Research](https://mlsanity.com/paper/2604.13018/) (2026)
- [AIBuildAI: An AI Agent for Automatically Building AI Models](https://mlsanity.com/paper/2604.14455/) (2026)
- [KAPSO: A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization](https://mlsanity.com/paper/2601.21526/) (2026)
- [Synthetic Sandbox for Training Machine Learning Engineering Agents](https://mlsanity.com/paper/2604.04872/) (2026)
- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [Can We Predict Before Executing Machine Learning Agents?](https://mlsanity.com/paper/2601.05930/) (2026)
- [AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems](https://mlsanity.com/paper/2606.26859/) (2026)
- [Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering](https://mlsanity.com/paper/2607.28568/) (2026)
- [EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale](https://mlsanity.com/paper/2604.17406/) (2026)
- [DS-STAR: Data Science Agent for Solving Diverse Tasks across Heterogeneous Formats and Open-Ended Queries](https://mlsanity.com/paper/2509.21825/) (2025)
- [LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis](https://mlsanity.com/paper/2605.30434/) (2026)
- [ScienceFlow: A long-horizon agent for ML research, scientific discovery and beyond](https://mlsanity.com/paper/2608.14354/) (2026)
- [ReX-MLE: The Autonomous Agent Benchmark for Medical Imaging Challenges](https://mlsanity.com/paper/2512.17838/) (2025)
- [What Do Evolutionary Coding Agents Evolve?](https://mlsanity.com/paper/2605.20086/) (2026)
- [AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models](https://mlsanity.com/paper/2605.27873/) (2026)
- [SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms](https://mlsanity.com/paper/2603.04873/) (2026)
- [OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces](https://mlsanity.com/paper/2605.08904/) (2026)
- [Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty](https://mlsanity.com/paper/2606.25198/) (2026)
- [Towards Autonomous and Auditable Medical Imaging Model Development](https://mlsanity.com/paper/2607.10522/) (2026)
- [Recovering Wasted Compute in Autoresearch Agents](https://mlsanity.com/paper/2608.10424/) (2026)
- [What is Missing from AI Post-Training AI: An Empirical Analysis](https://mlsanity.com/paper/2608.19072/) (2026)
- [VTOS: Learning to Orchestrate Vision Tools by Co-Searching Solutions and Observers](https://mlsanity.com/paper/2606.20728/) (2026)
- [Evolving Executable Pipeline Programs for AutoML with Language Models](https://mlsanity.com/paper/2608.16416/) (2026)
- [TraceML: An Empirical Analysis of Human-Agent Planning in Machine Learning Development](https://mlsanity.com/paper/2608.26086/) (2026)
- [DS-Lighting: Making Agent Harnesses Explicit for Data-Science Automation](https://mlsanity.com/paper/2608.28590/) (2026)
- [PrimeScientist: Strategic Allocation of Research Effort in Autonomous Research](https://mlsanity.com/paper/2609.17846/) (2026)
- [Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models](https://mlsanity.com/paper/2609.22097/) (2026)

## Abstract (arXiv)

Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress. Although crowd-sourcing platforms alleviate some challenges, high-level machine learning engineering (MLE) tasks remain labor-intensive and iterative. We introduce R&D-Agent, a comprehensive, decoupled, and extensible framework that formalizes the MLE process. R&D-Agent defines the MLE workflow into two phases and six components, turning agent design for MLE from ad-hoc craftsmanship into a principled, testable process. Although several existing agents report promising gains on their chosen components, they can mostly be summarized as a partial optimization from our framework's simple baseline. Inspired by human experts, we designed efficient and effective agents within this framework that achieve state-of-the-art performance. Evaluated on MLE-Bench, the agent built on R&D-Agent ranks as the top-performing machine learning engineering agent, achieving 35.1% any medal rate, demonstrating the ability of the framework to speed up innovation and improve accuracy across a wide range of data science applications. We have open-sourced R&D-Agent on GitHub: https://github.com/microsoft/RD-Agent.