This week in Multimodal

Visibility-Aware Reference Generation for Benchmarking Visual 3D Reconstruction in Vegetation-Rich EnvironmentsNov 3
GSCV++: Lightweight 3D Gaussian Splatting Sequence Compression via Standard Video CodecsNov 3
Low-Density 3D Point Cloud ClassificationNov 3

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Pulse · app.dealroom.co

app.dealroom.co · Nov 4

Meta buys AI coding startup Conductor to speed up its developer tools

Pulse · msn.com

msn.com · Nov 4

Exclusive: Meta buys AI startup, joins race to own coding in show of outsized ambition

Meta has acquired Conductor, an AI coding startup that lets software engineers manage multiple coding agents simultaneously, sources familiar with the deal tell Axios exclusively. Why it matters: Meta is joining OpenAI and other AI giants in a race to own the tools developers use,

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Top picks this weekarXiv

arXiv

Nov 3

DreamX: Video Diffusion Model is a Universal Latent Action Learner

Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet invariant to changes in embodiment and appearance. Existing latent action models infer actions from visual transitions, but their representations often entangle the underlying action with irrelevant visual variations, limiting transfer across embodiments. We introduce DreamX, which repurposes a pretrained video diffusion model as a universal latent action learner. Our approach extracts compact latent actions from the video model's rich spatiotemporal features, compressing each latent video frame by $\boldsymbol{300\times}$ ($\boldsymbol{1{,}200 \rightarrow 4}$ tokens). In addition, by leveraging the video model's generative prior, these latent actions capture action-relevant dynamics disentangled from visual appearance. The resulting latent space serves as a shared action interface across embodiments (human and robot), environments, and action parameterizations. On LARY-Bench, DreamX achieves state-of-the-art performance among latent action models. Moreover, actions encoded in one setting remain effective in another: the latent…