How Width and Data Shape Generalization Scaling Laws in Quadratic Neural Networks
Understanding how performance scales jointly with model size and data is a central problem in modern machine learning...
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Understanding how performance scales jointly with model size and data is a central problem in modern machine learning...
Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. How...
The transition from static chat bots to autonomous agents--equipped with persistent memory, tool-use protocols, and m...
Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular...
Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis gene...
We present HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution. A...
Many two-player zero-sum games admit not a unique Nash equilibrium but a convex set of them: a polytope of profiles t...
Dexterous manipulation policies can solve individual skills, but composing them to perform multiple tasks with a sing...
Video generation models have emerged as a promising paradigm for embodied world simulation. However, both general-dom...
Training and evaluating robot policies in the real world is costly and difficult to scale. We introduce SimFoundry, a...
Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis gene...
We introduce an axiomatic evaluation framework for latent thought representations in LLMs, comprising metrics that ar...