AI
What’s new VideoAR is a new way to generate videos with AI that predicts the next frame, over and over, at multiple scales. By separating what happens within a frame (spatial) from what happens across frames (temporal), it aims to make video generation faster and more stable. * 3D multi-scale
AI
AI meets messy spreadsheets New research shows that large language models (LLMs) struggle when tables are subtly distorted—through small layout or labeling tweaks—even if the data is otherwise standard. * LLMs rarely notice or fix these distortions on their own. * Giving an explicit "watch out for table errors&
AI
LLMs with Retrieval-Augmented Generation (RAG) work better when they read lots of context — but long prompts are slow and pricey. Compressing the context helps, yet models often lose the thread and answer worse. ArcAligner is a lightweight module that helps models make sense of highly compressed context. It "aligns&
AI
Sometimes, vision-language AIs see what the prompt says, not what is in the picture. New research maps how that happens—and how to dial it down. Rudman and colleagues tested object counting with prompts that intentionally overstate what is in an image (for example, "describe four waterlilies" when
AI
As AI models start learning from their own outputs, they risk entering an echo chamber. A new study names this cycle the Self-Consuming Performative Loop (SCPL) and shows how it can warp model behavior over time. * What’s the loop? Deployed models shape what people ask and which data gets
AI
Personalized AI you can read, edit, and take anywhere Most AI systems hide your preferences in opaque vectors. This work makes them plain English instead. Your "profile" becomes a short text summary the model can use across apps and tasks. * Interpretable: you can see and revise what the
AI
Teaching AI to Follow the Rules—While Staying Creative GANs can generate realistic images and data, but they often ignore the “rules of the world.” Enter LTN‑GAN: a new framework that blends Generative Adversarial Networks with Logic Tensor Networks, so the generator learns to produce samples that look real
AI
Decision trees (and ensembles like random forests and gradient boosting) make powerful predictions—but their reasoning can be hard to follow, which is risky in healthcare, finance, and other safety-critical settings. This paper shows how to turn those predictions into clear, formal explanations using Answer Set Programming (ASP), a logic-based
AI
TL;DR: Giving AI agents a "what happens next?" simulator doesn’t automatically make them smarter. Researchers tested whether agents built on vision–language models can use a generative world model—a tool that predicts future states—to preview outcomes before acting. * Agents rarely choose to simulate: in
AI
Building images and videos in ComfyUI is like wiring LEGO blocks — powerful, but easy to break when workflows get long and complex. ComfySearch is a new AI agent that automatically explores, reasons, and assembles ComfyUI workflows. It builds step by step, checks whether each piece runs, and adjusts on the
AI
Teaching video AIs to sweat the small stuff Text-to-video models often miss human-preferred details or waste compute learning from vague, whole-video feedback. LocalDPO, a new training recipe, targets the exact frames and regions that need improvement—no human labels or extra critic model required. * How it works: Use a real,
AI
Deep neural networks are powerful—but often inscrutable. xDNN(ASP), a new method by Ly Ly Trieu and Tran Cao Son, turns a trained network into a human-readable set of logical rules using Answer Set Programming (a logic-based AI method). Unlike many explainability tools that only highlight which inputs mattered