Eiso Kant(@eisokant):Leaving image generation to diffusion but reasoning to an LLM is 🤩 Now take a step further and think about it in the context of robotics with spatial-action models. Work combining modalities is still in the early days.

2026.08.24 09:53

Leaving image generation to diffusion but reasoning to an LLM is 🤩 Now take a step further and think about it in the context of robotics with spatial-action models. Work combining modalities is still in the early days.

Tianwei Yin@TianweiY

2026.06.03 20:07

8/ With that, we reframed multimodal generation as structured text/code generation. Diffusion just renders pixels. Planning, logic, reasoning all live in the LLM — so training looks like normal LLM training, and inherits all benefits of it: data + model scaling, reasoning, RL, tool use.

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