做AI论文阅读工具或学术自动化智能体的开发者，这个约定能解决子声明引用和范围误判的痛点，值得直接参考或贡献。
论文提出了一种名为paper.json的轻量级JSON文件约定，旨在解决LLM智能体阅读学术论文时的常见问题，如子声明无法细粒度引用、范围过度扩展、图表命令隐藏在代码库中。该约定包含五个核心约定：稳定声明ID（C1）、明确的不声明列表（C2）、精确的每图shell命令（C3）、稳定定义ID（C5），以及最低可行合规性（C4）。作者声称，为已完成的论文手动编写符合规范的JSON文件可在1小时内完成。论文本身已实现合规，并通过了验证测试。该工作开源在GitHub上，为AI驱动的论文阅读和自动化提供了标准化接口。
paper.json: A Coordination Convention for LLM-Agent-Actionable Papers
LLM agents routinely serve as first (and sometimes only) readers of academic papers, skimming for sub-claims, extracting reproducibility steps, and generalizing scope. Standard prose papers produce recurring failures in this role: sub-claims that cannot be cited at sub-paper granularity, scope overextension beyond what the paper tests, and figure commands buried in codebases rather than the paper itself. We propose `paper.json`, a companion JSON file that travels with the PDF and addresses each failure with a lightweight convention: stable claim IDs (C1), an explicit does-not-claim list (C2), exact per-figure shell commands (C3), and stable definition IDs (C5). A fifth convention (C4) holds that minimum viable compliance, hand-written JSON alongside the PDF, is achievable in under an hour for a finished paper without touching the human-readable output. C1, C2, C3, and C5 are open invitations: an agent that reads a compliant paper and acts on it produces evidence for or against them. This paper is itself compliant: `uv run validator.py paper.json --against paper.typ` passes. Repo: https://github.com/arquicanedo/paper-json