World Wire

NVIDIA introduced open technologies for building always-on AI agents from systems of specialized models. Two artifacts shipped together. Nemotron 3.5 Lightning is a lightweight, customizable open model built for high-volume agentic tasks, and NeMo Switchyard is an open source routing library that directs each step of an agent workflow to the most capable and efficient model available. The problem both address is structural: long-running agents spend most of their time on tool calls, result validation, and subagent delegation, and sending every one of those steps to a frontier reasoning model adds cost and latency. Lightning is a 30B mixture-of-experts model with 3B active parameters, built on a hybrid Mamba-2 + MoE + Attention architecture with a 1M-token context window. NVIDIA reports up to 4x faster output speed than similar-sized models, and 30% faster completion of 10,000 PinchBench tasks than Qwen3.6 35B at comparable accuracy. Many industry players like CrowdStrike, Harvey, Co...

Object removal models have improved faster than the metrics used to judge them. Diffusion erasers now reconstruct shadows, reflections and occluded structure convincingly, yet PSNR, SSIM, LPIPS, ReMOVE and CFD frequently rank their outputs the wrong way. The root cause is structural: erasure is an ill-posed, one-to-many task, so no single ground truth exists to compare against. A team from MiLM Plus, Xiaomi Inc. has released PROVE (Perceptual RemOVal cohErence), accepted at ACM MM 2026 , to close that gap. PROVE pairs two perception-aligned metrics — RC-S for spatial coherence and RC-T for temporal consistency — with PROVE-Bench , a two-tier real-world video benchmark. Both metrics score the edited region locally, using sliding-window Maximum Mean Discrepancy over DINOv2 features, and neither requires a reference video. Is it deployable? Yes — as an evaluation harness, not a product feature. PROVE ships as an Apache 2.0 PyTorch repo with one CLI entry point ( run_prove...

Video production is shifting as social clips, ad creative and film pre-visualization move from cloud to local GPUs. LTX today released LTX-2.5 , an open weights world model for video generation, real-time applications, and physical AI, built for exactly that shift. LTX optimized the model for local inference on NVIDIA RTX GPUs and NVIDIA DGX Spark , cutting VRAM requirements so a frontier world model runs on hardware creators already own. The release anchors NVIDIA’s month-long local AI series , launched the same day as its open Nemotron 3.5 Lightning agent model. The signal from both: open models, accelerated locally, are becoming default production infrastructure. What Local Generation Changes for Creators LTX-2.5 puts something in creators’ hands that used to sit behind a studio door: real consistency. Native multishot generation renders a whole sequence as one coherent piece, holding a character’s look shot to shot, fixing the glitching that made ear...