This week in Multimodal

Occlusion for Free: Detecting Occlusion from the Disagreement Between Two Monocular Distance CuesNov 4
PixelSpline: Continuous Space-Time Video Super-Resolution via Endpoint-Anchored Shared Pixel TrajectoriesNov 4
LongBanana: An Expert-Verified Benchmark for Long-Context Multi-Reference Image SynthesisNov 4

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Pulse · technode.com

technode.com · Nov 5

Moonshot AI releases Kimi K3.1 Pro model weights

Moonshot AI has released the weights of Kimi K3.1 Pro on Hugging Face, making its flagship multimodal model available for independent deployment. The

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arXiv

Nov 4

From Pixels to Actions - Towards Native Vision-Language-Action Policies

Vision-language-action models are typically built on a simple premise: perception is first compressed by visual encoders, and action reasoning begins only afterwards. Yet manipulation often hinges on precisely the visual signals most vulnerable to this abstraction, e.g., local geometry, boundaries, depth, and subtle state changes. This raises a fundamental question: should action policies inherit a fixed visual endpoint, or directly access evolving visual computation? We introduce NEO-VLA, a structurally encoder-free vision-language-action framework. Images are tokenized by lightweight patch convolutional layers and evolve jointly with language throughout the backbone, allowing a flow-matching action expert to draw from instruction-conditioned visual representations at different depths. This simple change reveals a consistent pattern. Native architectures outperform encoder-based counterparts under controlled comparisons; full fine-tuning reduces but does not erase the gap, and additional scale alone cannot recover it. More strikingly, layer-wise probing shows that the action-critical signals often peak well before the final visual representation. Across diverse manipulation…