Europe's AI Act says makers of general-purpose AI must mark and detect AI-generated content - and those marks must be reliable, interoperable, effective, and robust. But what does that mean in practice? A new paper by Thomas Souverain maps today's watermarking tech for large language models
AI
Build production-ready AI coding agents, faster Meet the OpenHands Software Agent SDK—a toolkit for turning agent ideas into dependable software-engineering assistants. * Flexible by design: start with a few lines of code, then add custom tools, memory, and more. * Secure and portable: native sandboxed execution, seamless local→remote runs, built-in
Analog chips connect electronics to the real world—but sizing their circuits is still a slow, trial-and-error craft. AnaFlow introduces a different approach: a team of specialized, LLM-powered agents that read a circuit’s topology, understand design goals, and iteratively tune device parameters with human-readable reasoning. An adaptive simulation strategy
Smarter, more transparent fact-checking Large language models sound confident, but not always correct. Knowledge graphs are precise but can miss facts. This study combines both—plus a selective web-search agent—into one interpretable fact-checking pipeline. * Step 1: Rapid one-hop lookups in DBpedia to grab trusted facts. * Step 2: An LLM
GenerativeAI
Build stories like a graph What if writing felt like assembling LEGO? This research introduces a node-based editor that lets you craft multimodal stories—mixing text, images, audio, and video—one beat at a time. * Graph-first storytelling: Each scene is a node you can expand, swap, or refine with natural-language
RAG
Which LLM wins at RAG Q&A? RAG (Retrieval-Augmented Generation) helps reduce hallucinations by grounding answers in source documents. This study tested five 7B-class models on computer science literature Q&A to compare accuracy and speed. * GPT‑3.5 + RAG answered both yes/no and long-form questions effectively.
AI
Ever read code and wonder "why does this exist"? This research taps into GitHub's natural-language breadcrumbs like pull requests, issues, and commit messages to help AI explain code with real project context. How it works: * Gather relevant GitHub discussions and structure them. * Explain the code'
AI that thrives on bluffing Researchers built Solly, an AI that learns Liar’s Poker—a fast, bluff-heavy game with many players and hidden info. Instead of studying only two-player poker, Solly tackles truly multi-player dynamics. Trained via self-play with a model-free, actor-critic deep reinforcement learning approach, it taught itself
AI
Encrypted LLM chats can still leak their topic Whisper Leak shows that even with TLS, streaming responses create telltale packet size and timing patterns. Across 28 popular models, these fingerprints let an eavesdropper classify prompt topics with near-perfect accuracy, flagging sensitive ones like "money laundering" and even recovering
Generative AI isn’t just making images—it’s reshaping Extended Reality (XR across AR/VR/MR). This new systematic review screens recent work (2023–2025) and distills 26 studies on how XR taps generative AI, using a PRISMA approach. * Where it’s used: on-the-fly content creation, smarter virtual agents,
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