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Tuesday

  6. Shao Meng

    Viggle Releases JST-2, a 3D-Native World Model

    Viggle has released JST-2, building its entire model stack in 3D space and representing the world with Physics-Native Tokens that carry xyz coordinates, attribute vectors, and dynamics vectors, rather than implicitly inferring physical laws from video pixels. The architecture consists of three parts: the JST encoder, JST generator, and JST decoder. A single forward inference generates a complete 3D scene in about 1 second, at a cost below $0.01 (B200), and supports up to 64 input views as a spatial context window. Blind human preference tests published by Viggle show that character generation wins 58.4% against Meshy 7.1 and 73.2% against Tripo H3.1, while video-generated motion wins 89.1% against DeepMotion. JST-3 plans to extend the learned dynamics to object interaction.

    Viggle has released JST-2, building its entire model stack in 3D space and representing the world with Physics-Native Tokens that carry xyz coordinates, attribute vectors, and dynamics vectors, rather than implicitly inferring physical laws from video pixels. The architecture consists of three parts: the JST encoder, JST generator, and JST decoder. A single forward inference generates a complete 3D scene in about 1 second, at a cost below $0.01 (B200), and supports up to 64 input views as a spat

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    #hot-signal#Model releases#Multimodal#Video
  12. Ant Group's inclusionAI: New GitHub repository

    Ant Group's inclusionAI publishes the sglang_ling_v3 repository on GitHub

    Ant Group's inclusionAI launches the sglang_ling_v3 repository on GitHub. Its contents are based on the open-source inference framework SGLang, which targets large language models, vision-language models, and diffusion models, and is optimized for agent workloads, large-scale serving, and RL rollout. The repository README shows that SGLang supports GPU, TPU, NPU, CPU, and Apple Silicon, and includes the built-in image and video generation engine SGLang Diffusion.

    PyPI version

    Ant Group's inclusionAI launches the sglang ling v3 repository on GitHub. Its contents are based on the open-source inference framework SGLang, which targets large language models, vision-language models, and diffusion models, and is optimized for agent workloads, large-scale serving, and RL rollout. The repository README shows that SGLang supports GPU, TPU, NPU, CPU, and Apple Silicon, and includes the built-in image and video generation engine SGLang Diffusion.

    Matched fieldsBody

    #engineering#English text is being prepared.#English text is being prepared.#Open-source ecosystem

Monday