PaperCompiler: Faithful Paper-to-Code Generation via Repository-Level Specification Compilation

2026-09-02

AI Summary

为解决科研论文到代码库实现的忠实转换难题，PaperCompiler通过编译论文中的证据生成明确的实现规范，从而提高代码生成的准确性与一致性。

Abstract

Faithfully translating research papers into repository-level implementations remains challenging because papers often describe methods at a high level, leave implementation assumptions implicit, and require generated repositories to preserve method logic, evaluation protocols, and cross-file consistency. Despite recent advances in paper-to-code agents, their intermediate outputs are often presented as free-form plans or summaries that downstream coding agents may ignore, reinterpret, or compress, leading to algorithmic simplification and inconsistent repository structure. To address these challenges, we introduce PaperCompiler, a paper-to-code generation framework that compiles paper-grounded evidence into explicit repository-level implementation specifications. PaperCompiler grounds implementation-relevant evidence while preserving source provenance and distinguishing paper-supported, inferred, externally delegated, and unresolved information. The resulting specifications encode non-degradation requirements, ownership assignments, cross-file dependencies, and file-level constraints. Repository generation proceeds under these compiled specifications while retaining flexibility over local engineering choices not fixed by the paper. PaperCompiler outperforms strong baselines on Paper2CodeBench, achieving a 13.8% relative improvement in reference-based fidelity (from 3.64 to 4.15) and reducing high-severity evaluator critiques (from 13.2% to 6.1%).

Problem

• paper-to-code
• repository-level implementation
• cross-file consistency
• implementation assumptions
• algorithmic simplification

Innovation

• repository-level implementation specifications
• non-degradation requirements
• cross-file dependencies