Researchers at NVIDIA have introduced Spatial-IQ, a diagnostic framework designed to deconstruct the spatial intelligence of multimodal large language models (MLLMs). Unlike existing benchmarks that treat models as black boxes, this approach decomposes object counting in stacked 3D structures into nine perceptual and cognitive sub-tasks aligned with human developmental stages.
Researchers present Cluster-Guided Vector Quantization (CGVQ), a method designed to improve the rate-distortion performance of Gaussian primitive-based image compression. The approach partitions Gaussian parameters into homogeneous groups prior to quantization, addressing the inefficiency caused by storing large numbers of floating-point parameters per primitive.
Researchers analyze gameplay videos from the competitive shooter game Delta Force to extract and understand emergent non-verbal communication, such as gestures and movement patterns. This work addresses a gap in prior studies that have largely focused on MOBA games or verbal coordination in other shooters.
Researchers at NVIDIA are addressing the difficulty of scheduling competitive group play and recreating lab environments by adopting field experiment methodology. This approach integrates experimentation directly into tournament structures, creating hybrid tournament-experiments.
Researchers introduce The AI Telco Engineer (AITE), a framework that uses large language model-driven evolutionary search to autonomously design algorithms for complex communication problems while navigating performance-complexity tradeoffs.
NVIDIA has introduced CTRL-G (Controllable Generative Graphics for Games), a framework designed to address the challenge of controlling generative AI systems within interactive game environments.
Anthropic introduced Claude Opus 5 as a more efficient model that approaches the capabilities of Claude Fable 5 at half the price, becoming the default for Claude Max. Concurrently, NVIDIA is advocating for US government policies to support open-weight AI models to foster innovation and enhance American leadership in the sector.
NVIDIA has released Cosmos 3 Edge, a 4-billion-parameter open world model designed to deliver data center-level performance on memory-constrained edge systems. The model enables robots and vision AI agents to understand their surroundings, reason in real time, and generate actions directly on devices such as NVIDIA Jetson modules.
NVIDIA has released Nemotron 3 Embed, a collection of open and commercially available embedding models designed to improve retrieval quality for production-scale RAG, agentic retrieval, code retrieval, and agent memory. The collection features an 8B model that ranks #1 overall on the RTEB leaderboard, alongside efficient 1B variants optimized for deployment.
NVIDIA Labs has open-sourced NOOA (NVIDIA Object-Oriented Agents), a model-agnostic Python framework that consolidates agent development into a single Python class. This approach replaces fragmented workflows involving prompt templates and workflow graphs by mapping methods to actions, fields to state, docstrings to prompts, and type annotations to enforced contracts.
NVIDIA has released Alpamayo 2 Super, a 34-billion-parameter vision-language-action (VLA) model designed for robotaxis and autonomous driving under the OpenMDW-1.1 license. The model targets long-tail events by combining a 32B Cosmos 3 Super Reasoner backbone with a 2.3B diffusion-based action decoder to generate trajectories, causal explanations, and meta-actions from multi-camera video.
NVIDIA has made the NemotronLabs-VoiceChat-11B voice chat model available on Hugging Face. The repository is described as supporting full duplex communication capabilities.
NVIDIA's NeMo team has released Molt, an open-source agentic reinforcement learning framework designed to reduce the complexity of algorithm iteration for researchers. The codebase is approximately 8.6K lines of RL code, significantly smaller than comparable frameworks like verl (62K lines) and slime (25K lines).
Lucebox has partnered with AMD to demonstrate a heterogeneous consumer hardware setup that outperforms the Nvidia DGX Spark in inference speed. The system combines an AMD Radeon AI PRO R9700 GPU with a Strix Halo processor to run the full 284B DeepSeek V4 Flash model.
ThunderAgent is a system that introduces a program abstraction for agentic LLM request scheduling to address KV cache thrashing in high-concurrency synthetic data generation. By treating agent workflows as schedulable programs rather than independent requests, it significantly improves throughput and reduces latency.
NVIDIA CEO Jensen Huang stated that an open-weight frontier model helped contain a security intrusion during the recent Hugging Face incident, whereas closed AI systems blocked essential forensic analysis. He cited this event as the primary motivation for establishing the Open Secure AI Alliance.
NVIDIA has introduced Cosmos-H-Dreams, a real-time, action-conditioned generative simulator for surgical robotics that distills the capabilities of its Cosmos-H-Surgical-Simulator into a causal student model. Served through NVIDIA's FlashDreams accelerated streaming-inference library, the system enables interactive control by a person or learned policy in a closed loop.
NVIDIA has released the Nemotron 3 Ultra model, which leads open models in both accuracy and efficiency for agentic Register Transfer Level (RTL) coding tasks.
SLAI introduces T-Rex, an end-to-end optimization framework for the full-parameter post-training of trillion-parameter-scale MoE models on Ascend NPU SuperPOD. Using the DeepSeek-V4 model family as the target workload, the system addresses memory pressure and communication overhead through hierarchical parallelism and kernel execution optimizations.
SLAI introduces T-Rex, an end-to-end optimization framework for the full-parameter post-training of trillion-parameter-scale MoE models on Ascend NPU SuperPOD. Using the DeepSeek-V4 model family as the target workload, the system addresses memory pressure and communication overhead through hierarchical parallelism and kernel execution optimizations.