PaperCompiler Turns Paper-to-Code Generation into Repository-Level Specification

Introduction

Generating a runnable repository from a research paper is considerably harder than translating equations into code. Papers frequently describe the central idea at a high level while leaving data processing, experiment setup, evaluation procedures, and engineering assumptions implicit. Coding agents must fill in those gaps, and their intermediate plans are often free-form summaries rather than enforceable implementation contracts. During generation, important details can therefore be compressed, reinterpreted, or omitted. The resulting repository may run successfully while no longer faithfully implementing the paper.

PaperCompiler tackles this problem by introducing a specification-compilation stage between the paper and the coding agent. Instead of asking an agent to follow a loose plan, it turns implementation-relevant evidence into explicit specifications for the entire repository.

Key points

• Evidence with provenance. Each implementation-relevant item is associated with its status: directly supported by the paper, inferred from it, delegated to external information, or unresolved. This distinction helps prevent assumptions from being mistaken for source-grounded requirements.
• Repository-wide organization. The specifications assign ownership to files or modules and record cross-file dependencies. This is intended to keep model logic, configuration, data handling, training, and evaluation aligned rather than optimizing each file in isolation.
• Non-degradation requirements. Requirements tied to the method and evaluation protocol can be expressed as conditions that the generated repository should not weaken. At the same time, choices not fixed by the paper remain open to ordinary engineering decisions.
• Reported benchmark gains. On Paper2CodeBench, the authors report that reference-based fidelity increased from 3.64 to 4.15, a relative improvement of 13.8%. The share of high-severity evaluator critiques fell from 13.2% to 6.1%.

Why it matters

The important idea is not simply to generate more code. It is to create an auditable path from claims in a paper to responsibilities and constraints in a repository. Provenance makes it easier to distinguish what the source actually specifies from what the system had to infer. File-level ownership and dependency information give a downstream coding agent a more operational representation than a conventional natural-language plan.

This is particularly relevant to reproducibility. A reproduction can diverge from the reference method through seemingly minor changes in preprocessing, training control flow, or metric computation. Preserving these relationships across files is therefore as important as implementing an individual algorithm.

PaperCompiler does not remove ambiguity from research papers. Its more practical contribution is to expose unresolved areas rather than silently burying them in generated code. The approach could become a useful intermediate representation for multi-step coding agents, although the supplied material does not establish how it performs across different paper types, large production repositories, or human-led development workflows. Those questions remain open for further evaluation.

Source: Hugging Face Daily Papers