paper.json: A Coordination Convention for LLM-Agent-Actionable Papers
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
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
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 5 of its 12 references that are in the map. First posted on arXiv 15 May 2026.
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5 of 12 references.
References · 5
- 2022OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and conceptscited by 177
- 2023ExpertQA: Expert-Curated Questions and Attributed Answerscited by 97
- 2021Packaging research artefacts with RO-Cratecited by 24
- 2023Evaluating Factual Consistency of Summaries with Large Language Modelscited by 10
- 2022Open Research Knowledge Graph:A System Walkthroughcited by 5
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- 2026PaperCompiler: Faithful Paper-to-Code Generation via Repository-Level Specification Compilation
- 2026Knows: Agent-Native Structured Research Representations
- 2026Code as Representation: A Compilable Parsing Paradigm for Academic Documents