AI
AI that understands your preferences PersonaAgent with GraphRAG is a new way to build language model agents that adapt to you. The agent learns a compact "persona" from your past choices and behaviors, stored in a knowledge graph. It also finds communities of related information across documents to
healthtech
Voice tech is entering the clinic—but we still judge automatic speech recognition (ASR) mainly by Word Error Rate (WER). That score can’t tell you if a mistake is harmless or could change a care plan. * What they did: Clinicians compared ground-truth and ASR transcripts from real doctor–patient
AI
Teach Robots to Learn Like People Building versatile embodied AI (agents that see, read, and act) is hard because real-world data is scarce and training is expensive. We introduce Deliberate Practice Policy Optimization (DPPO), a metacognitive "metaloop" that alternates between: * Supervised fine-tuning to expand general competence * Reinforcement learning
AI
Smarter, faster explanations for what you’re recommended Big AI models can explain why you’re shown a product or movie—but wiring them directly into recommendation engines often slows everything down and muddles goals. "Prism" takes a cleaner route: it splits the system into two jobs. * Oracle:
AI
Meet CLOZE Clinical notes are full of real-world details, but they’re hard for computers to use. CLOZE is a new AI framework that reads these notes to expand medical ontologies—think of them as maps of diseases, symptoms, and their relationships. * Zero-shot: works without extra training or labeled data.
AI
NutriScreener is a new AI method to help screen for child malnutrition quickly and at scale—using ordinary photos captured from multiple poses. How it works: it combines CLIP-based visual features, a retrieval “knowledge base” that surfaces context from similar cases, and a multi-pose graph attention network that reasons across
AI
AI can be impressively accurate in medicine—but dangerously overconfident when cases are ambiguous. MedBayes‑Lite offers a fix: a lightweight add‑on that helps clinical language models say how sure they are. How it helps, without retraining: * Estimates what the model doesn’t know (epistemic uncertainty) using Monte Carlo
Robotics
Robots that learn by watching you What if multi-fingered robots could pick up everyday skills just by watching people in the real world? A new framework, AINA, takes a big step toward that. Using lightweight Aria Gen 2 smart glasses, anyone can record natural demonstrations at home, in the office,
AI
AI images that "think" as they form Most image generators plan before they draw or fix mistakes after. Thinking-while-Generating (TwiG) lets models do something new: interleave short bursts of textual reasoning during generation—like thinking out loud while painting. As pixels appear, the model explains what to add
AI
Can we quickly tell if an image is AI-made — and even which generator produced it — without retraining huge models every month? This study says yes. The authors propose a two-stage system: * Learn a visual "fingerprint" space. A vision model trained with supervised contrastive learning separates real vs. synthetic
AI
The debate over AI consciousness is loud—and often confusing. This paper offers a simple map to sort arguments, so we can see where people disagree and how strongly they mean it. What does an objection target? * The system’s goals and capabilities (what it does) — the computational level. * The
AI
Building capable embodied AI is hard: real-world data is scarce and training is expensive. This paper introduces Deliberate Practice Policy Optimization (DPPO), a coach-like training loop that helps vision-language agents learn more from less. How it works: the system alternates between learning from examples (to expand skills) and trial-and-error reinforcement