PDF当死，ARA该立！论文是时候Agent原生了

TL;DR - A 37-author team (Stanford, Michigan, CMU, MIT; first author Jiachen Liu) proposes ARA (Agent-Native Research Artifacts), a machine-operable replacement for PDF papers that packages scientific logic, executable code, an exploration graph of successes/failures, and raw evidence so AI agents can understand, reproduce, and extend research. It matters because reproducibility details and failed paths — which agents cannot guess — are systematically stripped out of conventional papers.

• Motivating gap: only 45.4% of PaperBench's 8,921 expert reproduction requirements were fully specified in paper PDFs; the authors call the lost exploration the "narrative tax" and the missing setup details the "engineering tax."
• ARA is a four-layer knowledge package (scientific logic, executable code + environment/config, exploration graph, evidence layer) supported by a Live Research Manager, an ARA Compiler for converting existing PDFs/repos, and an ARA-native review pipeline that automates structural/reproduction checks while leaving novelty and taste to humans.
• Measured results: 93.7% vs 72.4% accuracy on 450 comprehension questions (81.4% vs 15.7% on failure-retrospective questions); 64.4% vs 57.4% difficulty-weighted reproduction success across 150 tasks from 15 ML papers, with the gap widening on harder tasks (+4.9/+5.6/+8.5 points).
• Limits acknowledged: extension results were mixed (ARA won 3 of 5 RE-Bench open tasks, lost 2), scope is confined to code-reproducible ML, access control/sandboxing is immature, and fabrication still occurred (1 ARA case vs 2 baseline); the paper itself was submitted as a PDF.