Helping data centers deliver higher performance with less hardware
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
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Synthetic Sandbox for Training Machine Learning Engineering Agents
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,…
NASTaR: NovaSAR Automated Ship Target Recognition Dataset
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…
Metriplector: From Field Theory to Neural Architecture
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…
Beta-Scheduling: Momentum from Critical Damping as a Diagnostic and Correction Tool for Neural Network Training
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) =…
Common Inpainted Objects In-N-Out of Context
arXiv:2506.00721v2 Announce Type: replace-cross Abstract: We present Common Inpainted Objects In-N-Out of Context (COinCO), a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets. By systematically replacing objects in COCO images through diffusion-based inpainting, we create 97,722…
Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
arXiv:2509.20349v2 Announce Type: replace Abstract: Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models often lack the physical consistency required in regulated environments. To bridge this gap, we introduce…
Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems
arXiv:2602.19967v3 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) provide a promising framework for solving inverse problems governed by partial differential equations (PDEs) by integrating observational data and physical constraints in a unified optimization objective. However, the ill-posed nature of…
Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks
arXiv:2604.03345v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) have recently emerged as a powerful architecture for various machine learning applications. However, their unique structure raises significant concerns regarding their computational overhead. Existing studies primarily evaluate KAN complexity in terms of…
From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
arXiv:2604.03350v1 Announce Type: new Abstract: Systematic exploration of Agent-Based Models (ABMs) is challenged by the curse of dimensionality and their inherent stochasticity. We present a multi-stage pipeline integrating the systematic design of experiments with machine learning surrogates. Using a predator-prey…