OverSeer is a multi-agent world model that augments egocentric agent views with auxiliary camera views. These fixed views act as a proxy for a world-centric representation, ensuring consistency between agents and reducing computational costs through delta-VAE compression of changes relative to a static reference image.
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The OverSeer model can be useful for improving consistency in multi-agent systems, such as in robotics or virtual environments.
We study how vision-language models (VLMs) can learn to think and actively explore in 3D space. Instead of providing multi-view images, videos, or 3D representations to the model, we preserve the native visual encoding of VLMs, i.e., a single image, one at a time, just like humans, and treat the 3D representation only as an environment with which the model can interact through actions (e.g., move left) and observations (rendered images).
6 days agoDaily PapersThe paper introduces Video-Point Model (VPM), a world model that conditions on multi-view video histories and language instructions to jointly forecast future videos from arbitrary viewpoints and 3D trajectories of persistent scene points. The model extends a pretrained world model with camera-conditioned multi-view processing that integrates observations across cameras and predicts how physical points move over time.
yesterdayDaily PapersMemT-1.0 is a new architecture of memory foundation models that internalizes memory as part of model computation. It uses Gated DeltaNet-2 with a stable token-level memory update mechanism and Mixture-of-Storage to scale native memory. MemT-1.0-Nano and MemT-1.0-Flash models outperformed parametric memory baselines and were competitive with retrieval-augmented methods.
2 days agoDaily PapersWaypoint-OPD uses an intermediate teacher checkpoint to compare early and late shifts in action preferences during policy transfer. It evaluates the consistency between early and late shifts and suppresses the endpoint shift when they conflict.
6 days agoDaily PapersThis paper investigates the extent to which sparse autoencoder (SAE) representations support behavioral compositionality. The study introduces SAE Additivity Residuals (SAR) to measure unexplained behavioral variation above a chosen interaction order. Experiments show that learned SAE coordinates exhibit lower first-order residuals compared to random rotations of the same subspace.
6 days agoDaily PapersThis work introduces Scene-Designer, an agentic framework that learns design intelligence through complementary design and critique experiences. Two datasets, Scene-OTA-35K and Scene-OTC-15K, were created for scene design and quality critique. A Scene-GRPO methodology was proposed to improve design decisions and reduce unnecessary corrections. Experiments demonstrate superior performance in physical validity, functional correctness, and layout coherence.
5 days ago