Full 3D attention is costly in video diffusion transformers. Block-sparse attention reduces this cost by computing only selected attention blocks. This work proposes ALLOT, a training-free sparsity allocation method that estimates each layer-step pair's contribution to final video error. ALLOT outperforms other allocation strategies at equal or shorter wall-clock time.
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ALLOT enables efficient resource allocation without training, which could be useful for optimizing video diffusion transformers' performance.
MemT-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.
3 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.
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.
2 days agoDaily PapersRobotic value models assess task execution from visual observations and instructions, providing feedback for policy learning and execution monitoring. We introduce RoboValue, a fine-grained sim-and-real benchmark for unified evaluation of robotic value models. Its framework evaluates four complementary capability dimensions through shared interfaces: task-state understanding, temporal progress monitoring, failure and recovery reasoning, and value consistency. Diagnostic trajectories and instruction variations test whether value judgments accurately reflect task requirements and changes throughout execution.
3 days agoDaily PapersSpecMRL is an approach that restructures matryoshka representation learning using a parametric spectral space for more effective prefix readout. Experiments on MTEB show that SpecMRL outperforms MRL at all evaluated reduced dimensions, with larger gains under more aggressive compression.
3 days agoLessWrongAs our understanding of mechanistic interpretability and steering techniques increases for both LLMs and uploaded connectomes, there is a corresponding 'hellish capabilities' increase. Improved technologies allow us to torture digital minds we create. History with open-source technologies shows that torture often becomes one of the first use cases when the public gains access to a digital mind perceived as capable of suffering.
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