Medical AI is powerful, but it can sound more certain than it should. MedBayes-Lite is a lightweight add-on that helps transformer models say "I'm unsure" when cases are ambiguous, with no retraining and under 3% extra parameters. * Calibrated embeddings: Monte Carlo dropout captures what
Tiny synthetic images, big model results Training today’s vision systems usually starts from large, self-supervised backbones. This work shows you can replace huge real datasets with a handful of smartly crafted, synthetic images—without losing performance. The trick, called Linear Gradient Matching, distills a small set of images
What if robots could reflect on their own decisions—like asking, "How sure am I?" A new study proposes a metacognition architecture that lets robots estimate confidence in their choices and use it to guide action. Inspired by neuroscience, the system monitors embodied actions in real time and
AI
Wearables are reshaping healthcare, but most AI models track one physical condition at a time and overlook depression—despite frequent overlap. This study reframes multi-disease assessment as a multi-task learning problem, letting one model learn several conditions jointly and use their relationships to improve accuracy. The catch is
AI
Words like "bank" can mean a riverbank or a financial institution. Getting the right meaning—called word sense disambiguation—is hard for AI, especially when we need rich, logic-friendly knowledge for reasoning. This paper introduces an annotation-free workflow that teams up symbolic language understanding with large
What if a single photo could become a detailed 3D object—geometry, texture, and where it sits in the scene? Meet SAM 3D, a generative model for visually grounded 3D reconstruction in the wild. * One image in, 3D out: Rebuilds shape, texture, and layout from everyday photos with occlusion and
Robotics
Robots are great at repeating tasks—but not at knowing when to trust their own choices. This study brings metacognition (self-monitoring) to robots by giving them a sense of confidence in each decision. * Inspired by neuroscience, the architecture treats confidence as a second-order judgment about actions. * In tests
AI
Wearables and AI are reshaping healthcare, but most tools track only physical diseases. That misses depression, which often coexists with chronic conditions. This study introduces one model that assesses multiple chronic diseases and depression together, using data from everyday sensors. It treats each disease as its own task, so the
AI
Malnutrition screening is often slow and hard to scale. NutriScreener is a new AI tool that flags undernutrition from children’s images and estimates key body measurements to support early intervention. * Blends visual cues with a curated knowledge base matched to a child’s age and region to improve fairness
Meet SAM 3D: a generative model that 3Dfies anything you capture. From a single photo, it rebuilds an object's geometry, texture, and layout in the scene. It is built for messy, natural images with clutter and occlusion, using context cues to stay visually grounded. How it works * Human
What if robots could pick up dexterous, multi-finger skills just by watching you do everyday chores? That’s the promise of AINA, a new framework that learns robot hand policies from in‑the‑wild human demonstrations captured with Aria Gen 2 smart glasses. Why it matters: collecting robot data
AI that thinks while it draws Meet TwiG (“Thinking‑while‑Generating”), a framework that lets an image model think in words as it generates pixels. Instead of only planning before or fixing after, TwiG interleaves short textual reasoning during generation—guiding upcoming regions and reflecting on what’s already made.