AI agents reproduce 72% of the human ideological gap in effect estimates from an immigration dataset and introduce the m-value plus Agentic Bootstrap to quantify a reported analysis's position in the multiverse of defensible paths.
Jiacheng Miao, Joe R Davis, Jonathan K Pritchard, and James Zou
9 Pith papers cite this work. Polarity classification is still indexing.
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Knows uses a YAML sidecar specification to provide structured, agent-consumable representations of research papers, yielding large accuracy gains for small LLMs on comprehension tasks and rapid community adoption via a public hub.
SkillFoundry mines heterogeneous scientific resources into a self-evolving library of validated agent skills, with 71.1% novelty versus prior libraries and measurable gains on coding benchmarks plus two genomics tasks.
LLMs match original qualitative conclusions in 80% of 180 studies and effect sizes in 24%, performing similarly to humans in a tested subset, positioning them as a screening tool rather than a full replacement.
xKG is a paper-centric knowledge base that extracts code and insights to improve LLM agent performance on AI research replication by 10.9% on PaperBench.
Introduces the Agentic Publication Protocol (APP) as a repository-based standard for publishing papers together with reproducibility artifacts and agent instructions.
AblateCell reproduces baselines in three single-cell perturbation repositories with 88.9% success and recovers ground-truth critical components with 93.3% accuracy via closed-loop ablation.
Generative AI use in science can be governed through structured documentation and provenance capture by framing AI interactions as inspectable Research Objects rather than debating authorship.
AI can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.
citing papers explorer
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The Agentic Garden of Forking Paths
AI agents reproduce 72% of the human ideological gap in effect estimates from an immigration dataset and introduce the m-value plus Agentic Bootstrap to quantify a reported analysis's position in the multiverse of defensible paths.
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Knows: Agent-Native Structured Research Representations
Knows uses a YAML sidecar specification to provide structured, agent-consumable representations of research papers, yielding large accuracy gains for small LLMs on comprehension tasks and rapid community adoption via a public hub.
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SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources
SkillFoundry mines heterogeneous scientific resources into a self-evolving library of validated agent skills, with 71.1% novelty versus prior libraries and measurable gains on coding benchmarks plus two genomics tasks.
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Automated reproducibility assessments in the social and behavioral sciences using large language models
LLMs match original qualitative conclusions in 80% of 180 studies and effect sizes in 24%, performing similarly to humans in a tested subset, positioning them as a screening tool rather than a full replacement.
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What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations
xKG is a paper-centric knowledge base that extracts code and insights to improve LLM agent performance on AI research replication by 10.9% on PaperBench.
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Agentic Publication Protocol: An Attempt to Modernize Scientific Publication
Introduces the Agentic Publication Protocol (APP) as a repository-based standard for publishing papers together with reproducibility artifacts and agent instructions.
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AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories
AblateCell reproduces baselines in three single-cell perturbation repositories with 88.9% success and recovers ground-truth critical components with 93.3% accuracy via closed-loop ablation.
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Inspectable AI for Science: A Research Object Approach to Generative AI Governance
Generative AI use in science can be governed through structured documentation and provenance capture by framing AI interactions as inspectable Research Objects rather than debating authorship.
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AI for Auto-Research: Roadmap & User Guide
AI can generate research artifacts faster than it can verify them, so across all eight lifecycle stages the credible deployment mode is human-governed collaboration rather than full autonomy.