Refactorcoderqa: Cloud-Edge Framework Achieves Enhanced LLM Performance across Multi-Domain Coding Question Solutions

The increasing demand for reliable code generation fuels research into large language models, and a team led by Shadikur Rahman from Algoma University, Aroosa Hameed from Carleton University, and Gautam Srivastava from Brandon University now presents a significant advance in this field. They introduce RefactorCoderQA, a new benchmark designed to rigorously test the ability of these models to solve coding problems across a diverse range of technical domains, including software engineering, data science, and machine learning. This research addresses limitations in existing benchmarks by utilising authentic coding challenges sourced from Stack Overflow, providing a more realistic assessment of performance. Through this benchmark, the team demonstrates that their fine-tuned model, RefactorCoder-MoE, achieves state-of-the-art accuracy, substantially exceeding leading open-source and commercial alternatives and offering improved interpretability and practical relevance in generated code solutions.

Multi-Agent System Improves Code Generation Performance

This research details a comprehensive study of RefactorCoder-MoE, a novel approach to code generation utilizing a multi-agent system. The work demonstrates a clear understanding of the challenges in automated code creation and offers a promising solution through the innovative use of GuideLLM, SolverLLM, and JudgeLLM. Evaluation across Software Engineering, Data Science, Machine Learning, and Natural Language Processing domains, employing both automated and human assessment, validates the findings against strong baseline models including GPT-4, DeepSeek-Coder, and CodeLLaMA. Providing the prompts used for each agent enables reproducibility of the results.

The results demonstrate that RefactorCoder-MoE consistently outperforms baselines in accuracy, clarity, and efficiency. While the work is strong, ablation studies could investigate the impact of removing JudgeLLM or simplifying the system. Exploring different prompt designs and providing a more detailed error analysis would further enhance understanding. Providing more details about the RefactorCoderQA dataset and discussing the computational cost of the multi-agent system would provide a complete picture of its performance characteristics. Overall, this is a strong research presentation with the potential to significantly advance the field of code generation and automated programming. The multi-agent approach is innovative, the evaluation is thorough, and the results are compelling.

Cloud-Edge Collaboration for Enhanced LLM Reasoning

To improve the reasoning and problem-solving capabilities of Large Language Models (LLMs), scientists engineered a novel cloud-edge collaborative architecture centered around a structured, multi-agent prompting framework. This system comprises three specialized components: GuideLLM, SolverLLM, and JudgeLLM, each with a distinct role in the coding process. GuideLLM, deployed at the edge, provides methodological guidance, while SolverLLM, hosted in the cloud, generates code solutions. Finally, JudgeLLM, an automated evaluator, assesses solution correctness and quality. To rigorously evaluate performance, the team introduced RefactorCoderQA, a comprehensive benchmark incorporating authentic coding challenges sourced directly from Stack Overflow.

This benchmark spans Software Engineering, Data Science, Machine Learning, and Natural Language Processing, addressing limitations found in existing benchmarks. Extensive experiments demonstrate that the fine-tuned model, RefactorCoder-MoE, achieves state-of-the-art performance with an overall accuracy of 76. 84%, validated by human evaluations. The study also evaluated system-level metrics, such as throughput and latency, to provide deeper insights into the performance characteristics of the proposed architecture.

RefactorCoderQA Benchmarks Coding Model Performance

Scientists developed RefactorCoderQA, a comprehensive benchmark constructed from 2,635 authentic coding questions sourced from Stack Overflow, to rigorously evaluate large language models across diverse technical domains including Software Engineering, Data Science, Machine Learning, and Natural Language Processing. This work addresses the limitations of existing benchmarks by utilizing real-world problems and a consistent input-output format, enabling structured prompting and objective evaluation of model performance. Extensive experiments demonstrate that the team’s fine-tuned model, RefactorCoder-MoE, achieves an overall accuracy of 76. 84% on this challenging benchmark, significantly surpassing the performance of leading baseline models.

The research introduces a novel cloud-edge collaborative architecture featuring a structured, multi-agent prompting framework designed to enhance reasoning and problem-solving capabilities. This framework comprises three specialized components: GuideLLM, which provides methodological guidance; SolverLLM, responsible for generating code solutions; and JudgeLLM, an automated evaluator assessing solution correctness and quality. GuideLLM delivers step-by-step guidance to effectively interpret and approach each problem, while SolverLLM generates accurate and executable code solutions following the structured guidance. JudgeLLM, built on GPT-4o, evaluates the generated code for correctness, clarity, and efficiency, providing detailed feedback. Measurements reveal that RefactorCoder-MoE achieves up to 83% accuracy on Machine Learning tasks, validated by human evaluations. The team has made the RefactorCoderQA dataset openly available, providing a valuable resource for the research community.

RefactorCoder-MoE Achieves Leading Coding Accuracy

This work presents a novel cloud-edge collaborative architecture designed to enhance the reasoning and problem-solving capabilities of large language models. Researchers developed a structured, multi-agent prompting framework consisting of three specialized components: GuideLLM, SolverLLM, and JudgeLLM, working together to address complex coding tasks. To rigorously evaluate and improve performance, the team introduced RefactorCoderQA, a comprehensive benchmark built upon authentic coding challenges sourced from Stack Overflow.