MemoCoder: Automated Function Synthesis using LLM-Supported Agents

• URL: http://arxiv.org/abs/2507.18812v1
• Date: Thu, 24 Jul 2025 21:23:44 GMT
• Title: MemoCoder: Automated Function Synthesis using LLM-Supported Agents
• Authors: Yiping Jia, Zhen Ming Jiang, Shayan Noei, Ying Zou,
• Abstract summary: We propose MemoCoder, a framework that enables collaborative problem solving and persistent learning from past fixes.<n>A central Mentor Agent supervises the repair process by identifying recurring error patterns and refining high-level fixing strategies.<n> Experimental results show that MemoCoder consistently outperforms both zero-shot prompting and a Self-Repair strategy.
• Score: 1.498158806172909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the widespread adoption of Large Language Models (LLMs) such as GitHub Copilot and ChatGPT, developers increasingly rely on AI-assisted tools to support code generation. While LLMs can generate syntactically correct solutions for well-structured programming tasks, they often struggle with challenges that require iterative debugging, error handling, or adaptation to diverse problem structures. Existing approaches such as fine-tuning or self-repair strategies either require costly retraining or lack mechanisms to accumulate and reuse knowledge from previous attempts. To address these limitations, we propose MemoCoder, a multi-agent framework that enables collaborative problem solving and persistent learning from past fixes. At the core of MemoCoder is a Fixing Knowledge Set, which stores successful repairs and supports retrieval for future tasks. A central Mentor Agent supervises the repair process by identifying recurring error patterns and refining high-level fixing strategies, providing a novel supervisory role that guides the self-repair loop. We evaluate MemoCoder across three public benchmarks -- MBPP, HumanEval, and LiveCodeBench -- spanning a range of problem complexities. Experimental results show that MemoCoder consistently outperforms both zero-shot prompting and a Self-Repair strategy, with improvements ranging from 3.1% to 12.1% in Pass@10 and from 1.4% to 14.5% in Pass@50, demonstrating its effectiveness in iterative refinement and knowledge-guided code generation.

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