Towards a Densing Law for User Representation Learning at Billion-Scale Capacity
User representation learning in real-world industrial scenarios is commonly scaled by increasing user amount, behavio...
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User representation learning in real-world industrial scenarios is commonly scaled by increasing user amount, behavio...
Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devi...
Robot policies receive heterogeneous observations at each decision step, yet sequence models differ in how they organ...
Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, ...
Open-ended language-model benchmarks usually inherit a judge: a human preference panel, another model, or a brittle e...
Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution path...
Training-free block-sparse attention can accelerate video transformers, but row-wise attention concentration does not...
We introduce Hydra-0, a generalist world model conditioned on action flow, which represents robot actions as pixel mo...
Population-level behavior in large-language-model (LLM) agents cannot be characterized by single-agent benchmarks. We...
We present PhysCaP, a Physics-Informed Code-as-Policy agent for active perception in robotic manipulation. While visi...
Game world models have recently demonstrated promising capabilities in generating visually coherent and action-contro...
Recently, there has been a great deal of research into improving AI methods and their application. The main focus is ...