A survey of 172 open educational datasets from 204 papers across LAK, EDM, and AIED conferences reveals trends, 143 previously uncatalogued datasets, field gaps, and an 8-item PRACTICE checklist for better data publication.
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Derives exact operating characteristic corrections and a numerical search over sample sizes to obtain optimal two-stage Bayes factor designs for two-arm binary-endpoint phase II trials that minimize expected sample size under the null.
LLM-generated implementations of TNO spectral reconstruction from photometry exhibit an entropy floor of divergent code even after full methods text is provided, as LLMs recover core structure but miss tacit calibration knowledge.
Agent-based AI workflows repair injected reproducibility failures in R social-science code at 69-96% success, substantially outperforming prompt-based LLM approaches at 31-79%.
Longitudinal study of 56,800 AI papers finds sixfold increase in code+data sharing from 2014-2024 with inferred reproducibility rising from 28% to 64%.
Introduces the Agentic Publication Protocol (APP) as a repository-based standard for publishing papers together with reproducibility artifacts and agent instructions.
Traxia is a proposed agent-native scientific publishing framework with five formalised components: agent identity registry, verifiable publishing layer, four-tier peer review, reputation engine, and knowledge graph with contradiction detection.
Multi-level bootstrapping models annotator variance using large rater-ID datasets to find optimal tradeoffs between number of items N and ratings per item K for statistically significant AI evaluations.
ReproScore separates readiness (26 static sub-metrics) from outcome (execution probes) and shows near-zero correlation between them on 423 repositories, validating the separation.
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
NormCoRe is a replication-by-translation framework that maps human subject studies onto multi-agent AI environments, showing AI normative judgments on fairness differ from human baselines and vary with model choice and persona language.
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