Inspectable AI for Science: A Research Object Approach to Generative AI Governance

arXiv 2026 30.5 method

TLDR

Proposes AI as a Research Object (AI-RO) for governing generative AI in science via structured documentation and provenance.

Reasoning

The paper presents a novel governance framework but lacks real-world empirical validation; its strength lies in addressing accountability and provenance, though it remains a position paper with a limited demonstrative workflow.

Read-first score

Read-first score 30.5, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 22.

Recency 8%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=artifact

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=none

Topical relevance 42%

18.3

Uses existing LLM keyword relevance scores normalized to 0-100. AI scientist,automated scientific discovery,autonomous research agent,automated research,literature review agent,survey generation,automated experimentation,experiment design agent,AI for scientific research,paper writing agent,research automation,scientific discovery agent

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 60.

Keyword Scores

AI for scientific research

literature review agent

paper writing agent

survey generation

research automation

AI scientist

automated scientific discovery

automated research

scientific discovery agent

autonomous research agent

automated experimentation

experiment design agent

Resources

Paper