AI
LLMs can write tidy code that passes unit tests—but can they build money-making agents that plan, bid, and deliver under pressure? This study pitted 40 LLM-coded agents (prompted with methods including “vibe coding,” i.e., high-level guidance) against 17 human-coded agents from grad CS students in a real-world-style challenge:
AI
Can AI write like Dickens with a single word? What if one token could cue an AI to channel Austen, Twain, or Melville? This study fine-tunes large language models so a minimal, single-token prompt triggers 19th-century novelist styles. * How they checked it: An AI detector trained on authentic sentences judged
AI
Computers can see and hear, but smelling the world has been out of reach. New research introduces New York Smells: a large, real-world dataset that pairs odors with images. * 7,000 smell-image pairs from 3,500 objects, indoors and outdoors — about 70x more objects than past datasets. * Benchmark tasks: match
AI
Accuracy isn’t the same as reliability. Meet PaTAS—a “trust meter” that runs in parallel with neural networks to quantify how much you should trust each prediction, using Subjective Logic. * How it works: PaTAS adds Trust Nodes and Trust Functions that propagate trust about inputs, parameters, and activations. A
AI
TL;DR An open-source platform helps creators check if their content was used to train large language models, without heavy compute or opaque workflows. What it does * Verifies if a specific work appears in LLM training datasets. * Improves similarity matching and dataset validation. * Cuts compute by 10-30% via efficient API
AI
If you’re a writer, artist, or publisher, you can now verify whether your content was used to train large language models—without a data center or a PhD. In “Copyright Detection in Large Language Models,” David Szczecina, Senan Gaffori, and Edmond Li introduce an open-source platform that makes copyright
AI
Fighting AI with AI How do we trust AI inside airplanes and self-driving cars? Deep neural networks are powerful, but opaque—and that makes traditional safety checks hard. This paper proposes using foundation models to make AI-enabled systems safer, from requirements to deployment. * REACT: uses Large Language Models to translate
6G
LLM-powered network agents often decide based on simple averages—ignoring rare but catastrophic events. In 6G, that bias can break service guarantees. This work introduces a risk-aware negotiation framework for 6G network slicing that teaches agents to plan for the worst, not just the mean. * Predict the full latency distribution
AI
Meet MAESTRO: an AI coach for teamwork Training teams of AI agents is hard: you must design the right "score" (rewards) and the right practice plan (curriculum). Get either wrong and learning stalls. MAESTRO uses a large language model (LLM) as an offline training architect—so it guides
AI
AI that designs enzymes—and explains why Researchers introduce Genie-CAT, an agentic large language model that acts like a digital lab partner for protein design. * Reads the literature via retrieval to ground its ideas. * Parses 3D structures from the Protein Data Bank. * Computes electrostatic potentials to assess physics. * Uses machine
Robotics
What’s new Meet SENTINEL, an end-to-end model that lets humanoid robots turn plain-language instructions into whole-body motion - no hand-crafted pipelines or teleop in the loop. How it works * Trains on a large dataset built by tracking human motions in simulation and pairing them with text descriptions. * Directly maps
AI
Meet a new way to write captions and hashtags for fashion photos—grounded in what is actually in the picture. * Detects multiple garments in an image with a YOLO-based model. * Extracts dominant colors and infers fabric and gender by retrieving similar products with CLIP and FAISS. * Packages these facts as