Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that…
Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that…
Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that…
Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that…
Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that…
Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model architecture problem. Most state-of-the-art vision encoders are enormous, and when you trim them down to fit on an edge device, they lose the capabilities that…
arXiv:2604.04872v1 Announce Type: cross Abstract: As large language model agents advance beyond software engineering (SWE) tasks toward machine learning engineering (MLE), verifying agent behavior becomes orders of magnitude more expensive: while SWE tasks can be verified via fast-executing unit tests,…
arXiv:2512.18503v3 Announce Type: replace-cross Abstract: Synthetic Aperture Radar (SAR) offers a unique capability for all-weather, space-based maritime activity monitoring by capturing and imaging strong reflections from ships at sea. A well-defined challenge in this domain is ship type classification. Due…
arXiv:2603.29496v2 Announce Type: replace-cross Abstract: We present Metriplector, a neural architecture primitive in which the input configures an abstract physical system — fields, sources, and operators — and the dynamics of that system is the computation. Multiple fields evolve via…
arXiv:2603.28921v2 Announce Type: replace Abstract: Standard neural network training uses constant momentum (typically 0.9), a convention dating to 1964 with limited theoretical justification for its optimality. We derive a time-varying momentum schedule from the critically damped harmonic oscillator: mu(t) =…
