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  1. Ai2 Blog

    Ai2 Releases MolmoMotion, a Language-Guided 3D Motion Prediction Model

    Ai2 releases MolmoMotion: by inputting video frames, 3D points on objects, and action descriptions, it can predict the trajectories of these points in 3D space over the next few seconds, outperforming existing prediction methods. It also open-sources the MolmoMotion-1M dataset (derived from 1.16M videos) and the PointMotionBench benchmark (2.7K video clips), and releases the model weights. In simulation, a control policy based on MolmoMotion achieved a 76.3% success rate on pick-and-place tasks, higher than the 56.0% achieved by one based on Molmo 2.

    Ai2 releases MolmoMotion: by inputting video frames, 3D points on objects, and action descriptions, it can predict the trajectories of these points in 3D space over the next few seconds, outperforming existing prediction methods. It also open-sources the MolmoMotion-1M dataset (derived from 1.16M videos) and the PointMotionBench benchmark (2.7K video clips), and releases the model weights. In simulation, a control policy based on MolmoMotion achieved a 76.3% success rate on pick-and-place tasks,

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    #lab#Model releases#Multimodal#Video

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  2. Tongyi Qianwen / QwenSelected

    Alibaba Qwen Releases Omni-Modal Model Qwen3.8-Omni-Flash

    Alibaba Qwen has released Qwen3.8-Omni-Flash, its first omni-modal model built around agent capabilities, with native support for audio-visual understanding, reasoning, and tool calling. The company says its audio-visual capabilities are close to Gemini 3.8 Flash, with agent performance improving by an average of 19.5 points on WildClawBench-MM and UniClawBench; it supports a 1M token context and uses 51.8% fewer tokens than static understanding on OmniVideoBench. Video input costs are about 89% lower than Qwen3.5-Omni-Plus, while Qwen-MM-Plugins is being open-sourced and Qwen-Live Harness is coming soon.

    Alibaba Qwen has released Qwen3.8-Omni-Flash, its first omni-modal model built around agent capabilities, with native support for audio-visual understanding, reasoning, and tool calling. The company says its audio-visual capabilities are close to Gemini 3.8 Flash, with agent performance improving by an average of 19.5 points on WildClawBench-MM and UniClawBench; it supports a 1M token context and uses 51.8% fewer tokens than static understanding on OmniVideoBench. Video input costs are about 89%

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    #company#Model releases#Multimodal#Agents

Wednesday

  2. Ant Ling: Developer BlogSelected

    Ant Ling Open-Sources Native Multimodal Model Ling-3.0-flash-VL

    Ant Ling has released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, it has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K Token context window. The company says native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, and it introduces a visual feedback loop of observe, act, verify, and correct for front-end replication, GUI Agent, and medical report interpretation. The model is now available for free trial on Ling Studio, and the BF16 and FP8 versions have been open-sourced on Hugging Face and ModelScope.

    English text is being prepared.

    Ant Ling has released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, it has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K Token context window. The company says native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, a

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    #engineering#Model releases#Multimodal#Open-source ecosystem

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