arXiv AI
Social-JEPA: Emergent Geometric Isomorphism
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Platonic Representation Hypothesis on World Models
arXiv:2608.23720v1 Announce Type: new Abstract: World models have demonstrated significant potential for perceiving and simulating complex environments. Despite their strong performance, the fundamen...
arXiv Computer Vision
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GAE: Learning a Geometry-Native Latent Space for 3D-Consistent World Generation
The paper introduces the Geometry‑Native Autoencoder (GAE), a compact latent space that can be decoded into appearance, depth, camera parameters, and point maps, enabling 3D‑consistent world generation. By reparameterizing a geometry foundation model’s features, GAE replaces traditional appearance‑centric latents and improves visual quality and 3D coherence, achieving significant reductions in FVD and camera‑trajectory error on benchmark datasets. The work demonstrates that a geometry‑native latent space can serve as a shared interface between perception and generation models.
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From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning.
arXiv AI
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Less Decoder is More Encoder: Geometric Representation Learning from Novel View Synthesis
arXiv:2610.03717v1 Announce Type: cross Abstract: This paper examines the role of Novel View Synthesis (NVS) in geometric representation learning. In principle, NVS should reason about 3D scene struc...
arXiv AI
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OmniDrive: An LLM-Choreographed Multi-Agent World Model with Unified Latent Co-Compression for Multi-View Driving Video Generation
arXiv:2606. 17536v1 Announce Type: cross Abstract: Generative world models for autonomous driving face two unresolved tensions: heterogeneous control injection, where free-form language, HD-maps, trajectories, and camera poses reside in incompatible representational spaces, and post-hoc cross-view fusion, where per-camera latents fail to encode global 3-D geometry.
arXiv Machine Learning
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Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
arXiv:2602.15382v3 Announce Type: replace-cross Abstract: Heterogeneous multi-agent systems combine models with different capabilities through a common communication interface. Exchanging internal st...