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