ARA uses LLMs to build workflow graphs linking sources, methods, and outputs in papers, then scores reproducibility, reaching ~61% accuracy on 213 ReScience C articles and outperforming priors on ReproBench and GoldStandardDB.
The ai imperative: Scaling high-quality peer review in machine learning
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AI lowers the cost of generating plausible scientific artifacts without lowering verification costs, so the paper proposes blueprints as typed graph components that decompose claims, evidence, and assumptions to enable cheaper downstream verification.
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ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review
ARA uses LLMs to build workflow graphs linking sources, methods, and outputs in papers, then scores reproducibility, reaching ~61% accuracy on 213 ReScience C articles and outperforming priors on ReproBench and GoldStandardDB.
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Toward an Engineering of Science: Rebalancing Generation and Verification in the Age of AI
AI lowers the cost of generating plausible scientific artifacts without lowering verification costs, so the paper proposes blueprints as typed graph components that decompose claims, evidence, and assumptions to enable cheaper downstream verification.