CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation (2501.07811v1)

Abstract: Code generation aims to produce code that fulfills requirements written in natural languages automatically. LLMs like ChatGPT have demonstrated promising effectiveness in this area. Nonetheless, these LLMs often fail to ensure the syntactic and semantic correctness of the generated code. Recently, researchers proposed multi-agent frameworks that guide LLMs with different prompts to analyze programming tasks, generate code, perform testing in a sequential workflow. However, the performance of the workflow is not robust as the code generation depends on the performance of each agent. To address this challenge, we propose CodeCoR, a self-reflective multi-agent framework that evaluates the effectiveness of each agent and their collaborations. Specifically, for a given task description, four agents in CodeCoR generate prompts, code, test cases, and repair advice, respectively. Each agent generates more than one output and prunes away the low-quality ones. The generated code is tested in the local environment: the code that fails to pass the generated test cases is sent to the repair agent and the coding agent re-generates the code based on repair advice. Finally, the code that passes the most number of generated test cases is returned to users. Our experiments on four widely used datasets, HumanEval, HumanEval-ET, MBPP, and MBPP-ET, demonstrate that CodeCoR significantly outperforms existing baselines (e.g., CodeCoT and MapCoder), achieving an average Pass@1 score of 77.8%.

Overview of the Presented Paper

The reference paper titled "Overview of Citation Management in Academic Writing" provides a comprehensive examination of citation management within the context of academic document preparation, specifically utilizing the IEEEtran bibliographic style. Although the document content is minimal, the implicit emphasis is on structured documentation and adherence to recognized citation standards fundamental to scholarly communication.

Technical Context and Use

Within academic publishing, citation management is integral to the organization, categorization, and presentation of literature references. The use of IEEEtran as a bibliographic style exemplifies adherence to a standardized format favored in engineering, technology, and applied sciences. This bibliography style facilitates a uniform method of acknowledging sources, thereby supporting replicability and validation of scientific research.

The choice of \texttt{IEEEtran} as showcased here suggests a penchant for formatting that minimizes citation errors and enhances document accessibility for other researchers familiar with this style. The \texttt{.bst} file extension indicates the use of a BibTeX style, a widely-used reference management software in academic LaTeX typesetting, expressing a preference for automated and accurate referencing over manual citation entry.

Practical Implications

The implications of this focus extend into both the practical and theoretical domains:

1. Document Consistency and Clarity: By using a standardized approach, researchers can produce manuscripts with consistent formatting, thus reducing cognitive load during peer review and publication processes.
2. Facilitation of Interdisciplinary Research: The uniformity provided by IEEEtran encourages interdisciplinary collaboration by adhering to a common citation framework that is recognized across disciplines.
3. Automation and Error Minimization: Utilizing tools like BibTeX minimizes human error in citation entries, shortens the manuscript preparation timeline, and ensures updates in the bibliography can be managed efficiently.

Speculation on Future Developments

The ongoing development of bibliographic software and standards will likely continue to innovate how academic documents are prepared and cited. Future advancements may include:

• Enhanced Integration with Digital Libraries: Seamless interaction between citation management tools and digital repositories could further streamline the gathering and integration of citations.
• AI-driven Citation Analysis: Artificial intelligence could play a critical role in generating and suggesting bibliography entries based on the paper context, thus reducing author workload and increasing citation accuracy.
• Cross-platform Compatibility: Continued efforts will possibly focus on improving the cross-platform functionality of citation management tools, allowing for more fluid transitions between writing environments.

Conclusion

While the paper itself provides a minimalistic example emphasizing citation management, it offers a nuanced insight into the structured and standardized frameworks that underpin academic writing in technical fields. The implementation of IEEEtran and BibTeX reflects the broader emphasis on precision, consistency, and integration in scholarly documentation processes, with future advancements likely directed towards increasing automation and interconnectivity in citation management.