AI Shorts

Researchers from Cornell and Google introduce a unified Regression Language Model (RLM) that predicts numeric outcomes straight from code strings—protecting GPU kernel latency, program reminiscence utilization, and even neural community accuracy and latency—with out hand-engineered options. A 300M-parameter encoder–decoder initialized from T5-Gemma achieves sturdy rank correlations throughout heterogeneous duties and languages, utilizing a single text-to-number…

Table of contents FP8 index → top-k selection → sparse core attention Lets Talk about it’s efficiency and accuracy Summary FAQs DeepSeek launched DeepSeek-V3.2-Exp, an “intermediate” replace to V3.1 that provides DeepSeek Sparse Attention (DSA)—a trainable sparsification path geared toward long-context effectivity. DeepSeek additionally decreased API costs by 50%+, constant with the acknowledged effectivity beneficial…

Can a single AI stack plan like a researcher, cause over scenes, and switch motions throughout totally different robots—with out retraining from scratch? Google DeepMind’s Gemini Robotics 1.5 says sure, by splitting embodied intelligence into two fashions: Gemini Robotics-ER 1.5 for high-level embodied reasoning (spatial understanding, planning, progress/success estimation, tool-use) and Gemini Robotics 1.5 for…

Meta FAIR launched Code World Model (CWM), a 32-billion-parameter dense decoder-only LLM that injects world modeling into code era by coaching on execution traces and long-horizon agent–atmosphere interactions—not simply static supply textual content. What’s new: studying code by predicting execution? CWM mid-trains on two giant households of remark–motion trajectories: (1) Python interpreter traces that document…