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.
Researchers introduce Video-DeepResearch (Video-DR), a framework extending multimodal agents from static images to continuous video streams, addressing modality bias and parametric knowledge leakage. The system employs a decoupled perception-exploration pipeline with stage-wise tool unlocking to enforce cross-frame visual grounding before web retrieval.
Shieldstral is a 3B open-weights multimodal safety classifier that frames content moderation as a policy-adaptive question-answering task, allowing it to accept plain-language policies at inference time without retraining. It unifies text and image safety evaluation and delivers calibrated safety scores across diverse benchmarks while running efficiently on a single 16GB NVIDIA GPU.
Douyin has released the technical report for its Multimodal Embedding (DME) model, which combines large-scale contrastive pre-training with latent reasoning to achieve strong discrimination and efficiency.
The article highlights Qwen3.8-Max's performance in a one-shot evaluation context, noting its ability to handle complex physical simulations.
MiniMax has made its MiniMax-H3 model available on Hugging Face. This general-purpose, omni-modal generative system supports the unified understanding of multimodal contexts composed of text, images, video, and audio.
Thinking Machines Lab has released Inkling-Small, an open-weights Mixture-of-Experts model with 276 billion total parameters and 12 billion active parameters. The model is trained to reason natively over text, images, and audio, featuring a 1M token context window and adjustable thinking effort.
The latest roundup of open model artifacts highlights significant releases from Thinking Machines and Tencent, alongside updates from Poolside and DeepSeek.
Hanyu (Olivia) Yu, a high school sophomore, is seeking an arXiv endorser in cs.LG or cs.CV to upload her independent preprint titled "AttnRoute-MoE: Attention-Prior Routing for Mixture-of-Experts Vision Transformers."
The llama.cpp project released build b10218, which includes the addition of minicpmv46 downsample support via pull request #25993. This update introduces a 4x ignore VIT merger and places the downsample mode inside the GGUF format.
This AI news roundup covers significant developments in pricing, open-weight models, and robotics infrastructure. OpenAI aggressively reduced GPT-5.6 costs while Thinking Machines released a compact multimodal model, and Google DeepMind expanded its robotics capabilities.
Microsoft has released Mage-VL, an efficient 4B-parameter multimodal foundation model for image and video understanding that uses a codec-native approach to streamline visual processing. The system separates video streams into anchor (I) frames and predicted (P) frames, retaining only patches where the codec allocates bits to reduce visual token consumption by over 75%.
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 introduce ClinFusion, a vision-centric multimodal large language model designed to address the challenges of deploying AI in clinical practice by unifying 2D and 3D medical image understanding. The system features a compositional cascaded vision encoder with a Cascade Spatial-Aware Locality Fusion operator and includes a new evaluation framework comprising MedIF-Bench and region-of-interest-grounded metrics.
Researchers introduce ClinFusion, a vision-centric multimodal large language model designed for holistic medical understanding that unifies 2D and native 3D image analysis. The system utilizes a compositional cascaded vision encoder with a Cascade Spatial-Aware Locality Fusion operator to address the challenges of heterogeneous medical imaging.
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.
The llama.cpp project released build b10142, which introduces preliminary vision support for the MiniMax-M3 model. This update implements a text-only port that reuses existing components from MiniMax-M2, including GQA with per-head QK-norm and partial rotary, DeepSeek-V3 style experts, and swigluoai activation.
Black Forest Labs has released FLUX 3, a multimodal foundation model that learns from images, videos, and audio within a single architecture. It is the first FLUX model to ship video, audio, and action prediction from one set of weights.
Induction Labs has released Photon-1, a sparse 106B-A5B mixture-of-experts transformer that learns to simulate desktop environments and play games by predicting future frames from raw video without action labels. The model uses finite scalar quantization to compress each frame into 960 discrete tokens, achieving over 100 times better compression than existing representations while preserving text and layout details.
FLUX 3 is introduced as a single multi-modal model designed to handle image, video, audio, and action-prediction tasks. The system aims to produce creations that are truer to life across various styles.