Alibaba Cloud has introduced Qwen3.8-Omni-Flash, a natively multimodal agentic model designed for real-world productivity tasks that substantially improves multimodal understanding and reasoning compared to previous omni models.
The Alibaba Qwen Team has released Qwen3.8-LiveTranslate, a next-generation real-time simultaneous interpretation model that listens to live speech and returns translated text and audio while the speaker is still talking. The release introduces a new Interleave architecture designed to reduce latency and improve translation quality.
inclusionAI has released the Realtime-Venus system on Hugging Face, featuring two checkpoints: Realtime-Venus-Omni and Realtime-Venus-Audio. The Omni checkpoint is a 9B audio-visual interaction model adapted from MiniCPM-o 4.5 that continuously watches and listens to decide when to respond.
Alibaba's Qwen team has released Qwen3.8-Omni-Flash, its first omni-modal model designed around agentic capabilities for audio-video understanding and tool use. The model accepts text, images, audio, and video inputs to return text outputs, featuring a 1M-token context window and native function calling.
DeepSeek has released DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts model designed to address the high costs of long-context computation and large KV caches in agentic workloads.
Tencent has released WeVisDoc, an end-to-end document parser for page images fine-tuned from Qwen3-VL models. The system converts page images into structured Markdown, including LaTeX formulas and HTML tables.
Tencent has released WeVisDoc-4B, an end-to-end document parser for page images that converts pages into structured Markdown with LaTeX formulas and HTML tables. Fine-tuned from Qwen3-VL-4B-Instruct, the model achieves an Overall score of 95.38 on OmniDocBench v1.6 and ranks first among compared parsers across all four reported settings.
Intern Large Models has released the Intern-S2-397B model, which is available on Hugging Face. The release includes day-0 support from vLLM for running the model.
InternLM has introduced Intern-S2-397B, a 397-billion parameter multimodal foundation model designed for scientific intelligence and long-horizon agents. The model scales across pre-training, reinforcement-learning task coverage, and interactive agent environments to enhance general reasoning and agentic capabilities.
IBM and NASA have open-sourced the NASA-IBM Lunar Foundation Model, an AI system that consolidates decades of multi-modal lunar data from US and Japanese missions into a single representation. Built on IBM's TerraMind architecture, the model integrates observations at varying scales and viewing angles to address challenges like complex lighting and data resolution.
DeepSeek has released its new model, DeepSeek V4-1 Flash. It is a multimodal Mixture-of-Experts (MoE) model featuring a 552 billion parameter backbone.
DeepSeek has officially released the DeepSeek-V4.1-Flash model, which serves as the smallest member of its new architecture family and features native multimodal visual understanding. This new architecture is designed to provide a higher capability ceiling, faster inference speeds, higher throughput, and better scaling potential for larger models.
inclusionAI has released the open-source Ling-3.0-flash-VL model, which extends the language and reasoning capabilities of its predecessor with native image and video understanding.
DeepSeek has opened internal beta testing for an intermediate version of its DeepSeek V4.1 Flash model, which is now available via API.
Google has launched agentic video understanding across its Gemini Flash models, replacing the previous static processing method with a system that navigates video timelines on demand. This change allows the model to decide what to watch, at what frame rate, and through which modality, rather than ingesting the entire timeline at a fixed one frame per second.
llama.cpp version 0.4.0 introduces initial architecture support for the Qwen3.8-Flash-Next and NVIDIA Nemotron-3-Puzzle-75B-A9B models, alongside a bump of the underlying ggml library to version 0.23.0.
Ling-3.0-flash-VL is a new model built on the Ling-3.0-flash architecture that introduces visual understanding and visual agent capabilities.
Researchers present VisCAD, a foundation model suite designed to provide broad generalization and strong capability for realistic industrial product design. The suite addresses the challenges of part-level generation from diverse inputs like renders and text, as well as assembly-level generation involving interacting parts.
Researchers introduce NeoMME, a family of 260M and 800M multilingual multimodal encoders that process text and raw image patches using a single bidirectional Transformer. Unlike generative visual language models, NeoMME does not rely on separate pretrained vision towers or causal decoders, training the entire model from scratch with a masked discrete-diffusion objective.
The v0.29.0rc2 release includes a bugfix for the SHM worker cache to properly handle prefix-covered items.