AI
Researchers introduce Jr. AI Scientist, an autonomous "junior researcher" that can read a baseline paper from a human mentor, spot limitations, propose improvements, run experiments, and draft a new paper. Unlike earlier "fully automated" attempts, it follows a clear research workflow and uses modern coding agents
ImitationLearning
What if AI could learn skills the way people do - by watching and copying? This new survey maps the fast-moving world of imitation learning (IL) in the deep learning era. * Data: from full expert trajectories to partial or unlabeled sequences. * New methods tackle big pain points: generalization, covariate shift,
GenAI
GenAI is reshaping genomics, proteomics, transcriptomics, structural biology, and drug discovery. This systematic review asks six big questions to map where GenAI helps most—and what’s next. * Where it works: Sequence analysis, molecular design, and integrative modeling often beat traditional methods via pattern discovery and realistic output generation. * Best
AI
AI agents promise to speed up discovery by doing the messy parts of research—forming hypotheses, writing code, running experiments, and analyzing results. But do they actually handle end-to-end projects? InnovatorBench is a new benchmark+platform that tests agents on realistic Large Language Model (LLM) research workflows. * 20 tasks across
AI
AI agents are getting good at coding and research, but they still struggle with long, domain-specific projects. Training them is hard: full-time human annotation is too costly, while pure trial-and-error rarely finds successful paths. Apollo is a new sampling framework that blends lightweight, asynchronous human guidance with strict action-level filtering.
AI
If your AI segments tumors, who is it serving—and who might it be failing? A new audit of the MAMA-MIA breast cancer segmentation dataset examines fairness not in classification, but in segmentation—the step that often feeds every downstream decision. * Researchers evaluated automated segmentation quality by age, ethnicity, and
ExplainableAI
Meet “Modules of Influence”: groups of features that drive AI decisions Most explainers like SHAP or LIME tell you which single features mattered for one prediction. This paper goes a step further: it builds an explanation graph across many predictions and uses community detection to reveal feature groups that act