AutoP2C: An LLM-Based Agent Framework for Code Repository Generation from Multimodal Content in Academic Papers (2504.20115v2)

Abstract: Machine Learning (ML) research is spread through academic papers featuring rich multimodal content, including text, diagrams, and tabular results. However, translating these multimodal elements into executable code remains a challenging and time-consuming process that requires substantial ML expertise. We introduce ``Paper-to-Code'' (P2C), a novel task that transforms the multimodal content of scientific publications into fully executable code repositories, which extends beyond the existing formulation of code generation that merely converts textual descriptions into isolated code snippets. To automate the P2C process, we propose AutoP2C, a multi-agent framework based on LLMs that processes both textual and visual content from research papers to generate complete code repositories. Specifically, AutoP2C contains four stages: (1) repository blueprint extraction from established codebases, (2) multimodal content parsing that integrates information from text, equations, and figures, (3) hierarchical task decomposition for structured code generation, and (4) iterative feedback-driven debugging to ensure functionality and performance. Evaluation on a benchmark of eight research papers demonstrates the effectiveness of AutoP2C, which can successfully generate executable code repositories for all eight papers, while OpenAI-o1 or DeepSeek-R1 can only produce runnable code for one paper. The code is available at https://github.com/shoushouyu/Automated-Paper-to-Code.

• The paper introduces AutoP2C, a multi-agent LLM framework designed to automate the Paper-to-Code (P2C) task by generating executable code repositories from the multimodal content of academic papers.
• AutoP2C employs a four-stage framework involving blueprint extraction, multimodal parsing, hierarchical decomposition, and iterative debugging to handle diverse paper content.
• Evaluation shows AutoP2C successfully generated executable code for all 8 test papers, significantly outperforming comparative models and achieving performance fidelity close to original implementations.

AutoP2C: An LLM-Based Agent Framework for Code Repository Generation from Multimodal Content in Academic Papers

In "AutoP2C: An LLM-Based Agent Framework for Code Repository Generation from Multimodal Content in Academic Papers," the authors present a sophisticated framework leveraging LLMs to address the challenging task of transforming the complex multimodal content of academic papers into executable code repositories. The paper initiates by outlining the prevalent issue within machine learning research, where rich multimodal content—encompassing textual descriptions, diagrams, equations, and tables—is manually converted into code, a process that demands significant expertise and time.

AutoP2C introduces a novel task, termed "Paper-to-Code" (P2C), aimed at automating this conversion process. Distinct from traditional code-generation tasks that often focus on translating textual descriptions into code snippets, P2C endeavors to synthesize complete, multi-file, structured code repositories that embody the algorithms and methodologies described in research papers.

Framework Components

The framework, AutoP2C, consists of a multi-agent system with four distinct stages:

1. Repository Blueprint Extraction: This initial stage involves analyzing established codebases to derive a standardized template or "architectural blueprint." This template serves as the foundational structure guiding the organization of future code repositories generated by AutoP2C. It captures common design patterns and file dependencies prevalent in high-quality ML code repositories.
2. Multimodal Content Parsing: Utilizing a combination of Optical Character Recognition (OCR) and Vision LLMs (VLMs), this stage processes the diverse elements within academic papers, such as text, diagrams, and tables. The framework integrates these modalities into a unified, implementation-focused representation, capturing cross-modal relationships crucial for accurate code generation.
3. Hierarchical Task Decomposition: Following content parsing, AutoP2C employs LLMs to break down complex code generation tasks into hierarchical subtasks. This decomposition ensures structured code generation by defining clear interfaces and dependencies, effectively managing complexity through a divide-and-conquer strategy.
4. Iterative Feedback-Driven Debugging: The final stage includes iterative testing and refinement cycles, ensuring the generated repository's functionality aligns with the specified algorithms. This process incorporates execution feedback to diagnose and rectify errors, enhancing both the reliability and performance of the output code.

Evaluation and Results

Evaluated over a benchmark comprising eight research papers from various ML domains, AutoP2C demonstrated its capability to generate executable code repositories consistently, a feat challenging for existing LLM-based methods. Notably, AutoP2C achieved successful code execution across all test papers, outpacing comparative models like OpenAI-o1 and DeepSeek-R1, which managed to produce functional code for only one paper. Strong numerical results underscored AutoP2C's efficacy, with relative performance metrics revealing close fidelity to original implementations, achieving performance levels in some cases exceeding the baseline provided by the original research code.

The paper further introduces novel evaluation metrics to assess the structural completeness of generated repositories—class completeness and function completeness—quantifying how accurately AutoP2C captures multimodal specifications from research papers.

Implications and Future Directions

The implications of this research are substantial, fostering advancements in research reproducibility and facilitating automated implementation of cutting-edge machine learning methodologies. Practically, AutoP2C could significantly ease the burden on researchers, providing a more accessible pathway to experiment replication and algorithm implementation.

Looking forward, areas for continued exploration include extending the framework's adaptability across diverse programming languages and incorporating user feedback loops to refine code generation processes further. Additionally, expanding the benchmark to encompass a broader spectrum of academic papers could enhance model robustness, potentially informing improvements in LLM capabilities regarding multimodal data synthesis and reasoning.

In conclusion, AutoP2C presents a significant step toward automating complex code generation from multimodal academic content, illustrating the immense potential of multi-agent systems augmented by LLMs in advancing machine learning research.