ResearchCodeAgent: An LLM Multi-Agent System for Automated Codification of Research Methodologies

• URL: http://arxiv.org/abs/2504.20117v1
• Date: Mon, 28 Apr 2025 07:18:45 GMT
• Title: ResearchCodeAgent: An LLM Multi-Agent System for Automated Codification of Research Methodologies
• Authors: Shubham Gandhi, Dhruv Shah, Manasi Patwardhan, Lovekesh Vig, Gautam Shroff,
• Abstract summary: We introduce ResearchCodeAgent, a novel multi-agent system to automate the codification of research methodologies.<n>The system bridges the gap between high-level research concepts and their practical implementation.<n>ResearchCodeAgent represents a significant step towards the research implementation process, potentially accelerating the pace of machine learning research.
• Score: 16.90884865239373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we introduce ResearchCodeAgent, a novel multi-agent system leveraging large language models (LLMs) agents to automate the codification of research methodologies described in machine learning literature. The system bridges 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.

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