AI Model2026
Generates high-fidelity images from text prompts using NVIDIA's 64B Cosmos3-Super multimodal foundation model. Integrates with Hugging Face Diffusers and vLLM‑Omni, is released under OpenMDW1.1 for commercial use, and is optimized for Physical AI workflows (robotics, AV, simulation).
Analyzes spatial representations in vision–language models and reveals a consistent vertical-position ↔ distance entanglement; introduces SpatialTunnel, a synthetic benchmark that exposes this perspective-driven shortcut, and provides code and a project page.
AI Video Papers2026
Performs training-free early-stage visual token compression inside the vision encoder to cut time-to-first-token (TTFT) and FLOPs for Video-LLMs. Introduces a decoupled spatial token selection strategy and reports up to 2.65× TTFT reduction and 61% FLOPs savings on LLaVA-OneVision-7B (NVIDIA A100) while preserving full-token accuracy — aimed at latency-sensitive video understanding.
Synthesizes high-quality targets for real-world image restoration by using multimodal foundation models (MFMs) to convert real low-quality photos into HQ references. Provides GGT-100K (103,707 LQ–HQ training pairs + 500 test pairs) with multi-stage quality control and demonstrates consistent generalization gains for a range of restoration models, especially for finetuning generative restorers.
AI Model2026
A GGUF-quantized, locally runnable build of Gemma 4 12B Unified (image-text-to-text) packaged by unsloth; preserves multimodal (image/audio) input support under an Apache-2.0 license and is compatible with common GGUF runtimes and Unsloth Studio.
Generates synchronized, streaming spatial audio from panoramic video and text prompts using a causal autoregressive diffusion transformer. Combines Spatial Video-Audio Contrastive (SVAC) alignment and online direct preference optimization (ODPO) to improve spatial perception, plus an automated annotation pipeline and public demos.
AI Dataset2026
Evaluates metric 3D spatial reasoning from single driving images via multiple-choice questions that require reconstructing scene geometry rather than relying on image-layout shortcuts. Each sample pairs a numbered-bbox image with a question, four choices, and the correct answer; images come from PlusAI and the dataset is CC BY 4.0.
AI Model2026
Generates and reasons about multimodal physical-world content—text, images, video, audio, and robot/action trajectories—conditioned on combinations of text, image, video and action inputs. The 64B “Super” variant targets Physical AI use cases and supports vLLM‑Omni, Diffusers, and action prediction.
Omnimodal world model that jointly processes and generates text, images, video, audio, and action trajectories for physical AI. Uses a mixture-of-transformers to combine autoregressive reasoning and diffusion-based multimodal generation; released open-source with checkpoints, datasets and benchmarks for robotics and simulation.
AI Agent Papers2026
Workflow-aware benchmark for autonomous medical-AI research that splits agent execution into five stages (Plan, Setup, Validate, Inference, Submit) and evaluates long-horizon runs across segmentation, image enhancement, VQA, report generation, and lesion detection with stage-level scoring.
Evaluates multimodal LLMs on streaming egocentric video for spatial intelligence using 1,680 human-annotated questions across 348 videos; organizes tasks into four hierarchical levels (perception → tracking → simulation → allocentric mapping) and highlights allocentric mapping as the main bottleneck.
Studies when and how to combine visual future rollouts from world models with abstract reasoning in multimodal LLMs. Proposes PF-OPSD — a teacher-student distillation that uses ground-truth future videos during training — and evaluates on two human-verified benchmarks, improving accuracy ≈10% while improving robustness to noisy rollouts.