Go to AAAI 2025 Workshop AI4Research homepageResearchCodeAgent: An LLM Multi-Agent system for automated codification of research methodologies
Keywords: LLM, Multi-Agent System, Research Automation, Code Generation
TL;DR: ResearchCodeAgent automates the codification of research methodologies in machine learning using LLM agents, generating high-quality, error-free code and enhancing coding efficiency across various tasks.
Abstract: In this paper we introduce ResearchCodeAgent, a novel multi-agent system leveraging Large Language Model (LLM) agents to automate the codification of research methodologies described in machine learning literature. The system tries to bridge the gap between high-level research concepts and their practical implementation, allowing researchers auto-generating code of existing research papers for benchmarking or building on top-of existing methods specified in the literature with availability of partial or complete starter code. ResearchCodeAgent employs a flexible agent architecture with a comprehensive action suite, enabling context-aware interactions with the research environment. The system incorporates a dynamic planning mechanism, utilizing both short and long-term memory to adapt its approach iteratively. We evaluate ResearchCodeAgent on three distinct machine learning tasks with distinct task complexity and representing different parts of the ML pipeline: data augmentation, optimization, and data batching. Our results demonstrate the system's effectiveness and generalizability, with 46.9\% of generated code being high-quality and error-free, and 25\% showing performance improvements over baseline implementations. Empirical analysis shows an average reduction of 57.9\% in coding time compared to manual implementation. We observe higher gains for more complex tasks. ResearchCodeAgent represents a significant step towards automating the research implementation process, potentially accelerating the pace of machine learning research.
Archival Option: The authors of this submission want it to appear in the archival proceedings.
Submission Number: 12
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