As AI becomes increasingly embedded in research and scientific publishing, transparency, accountability, and human oversight remain essential to safeguarding research integrity.

This study develops enhanced quantum solvers for an NP-complete Boolean satisfiability problem and reports empirical scaling advantages over leading classical solvers in large-scale simulations, with supporting validation on a superconducting quantum processor.

A generative model designs information metamaterials from meta-atoms to programmable arrays, enabling beam steering, focusing and holography with experimental validation and more than 1,000-fold faster holographic design.

This study presents AlignIF, which uses multiple structure alignments to capture evolutionary rules and conservation patterns across RNA families, enabling diverse, structure-guided designs of functional aptamers and ribozymes.

MutexaGPT turns plain-language enzyme-engineering intuitions into lead mutation designs through AI agent-orchestrated high-throughput molecular modeling, yielding experimentally validated improvements in enzyme specificity and cold activity.

This Resource introduces PathSegmentor—a foundation model that segments 160 pathological categories via natural language prompts, generalizes across datasets and clinical cohorts, and enables scalable, interpretable pathology image analysis.

The authors develop a physics-transfer learning framework that learns cortical folding physics from simple geometries and transfers it to complex brain structures, enabling accurate prediction of brain morphogenesis from limited data.

PULSE is an AI framework that uses patients’ longitudinal electronic health records to accurately generate within- and cross-modal data from routine clinical laboratory tests, enabling cost-effective precision medicine across diverse biomedical settings.

This work presents an efficient and modular framework called CrysVCD, which integrates chemical rules directly into the generative process for valence-constrained design.

The study finds that authors’ rankings of their own AI conference papers predict later citations better than peer-review scores, suggesting that self-rankings could provide a simple complement to peer review.

As AI becomes increasingly embedded in research and scientific publishing, transparency, accountability, and human oversight remain essential to safeguarding research integrity.

As large language models begin to plan and run experiments on their own, the reflex is to demand that they be interpretable before they are trusted. We argue that what grounds trust in autonomous science is not a view inside the model; it is provenance: a complete, re-openable record of what was reasoned, done and measured that can be audited and set right when it is wrong, making autonomy a corrective for the literature’s biases rather than a threat to rigor. Interpretability remains valuable, but the record is the gateway to trust.

Privacy, utility, and efficiency benchmarks have advanced privacy-preserving genome-wide association studies (GWAS) but often fall short of supporting organizational decisions about responsible data sharing. Future benchmarks should connect formal guarantees and assumptions to institutional risk, accountability, and decision making.

We highlight how AlphaFold2 has advanced protein design and inspired the development of artificial intelligence-driven infrastructures for scientific discovery.

Geographical disparities regarding the availability of GPU hardware are becoming a structural constraint on scientific participation itself and must be addressed.