B-DENSE: Branching For Dense Ensemble Network Learning
arXiv:2602.15971v1 Announce Type: new Abstract: Inspired by non-equilibrium thermodynamics, diffusion models have achieved state-of-the-art performance in generative modeling. However, their iterative sampling nature results in high inference latency. While recent distillation techniques accelerate sampling, they discard intermediate trajectory steps. This...
Fast Online Learning with Gaussian Prior-Driven Hierarchical Unimodal Thompson Sampling
arXiv:2602.15972v1 Announce Type: new Abstract: We study a type of Multi-Armed Bandit (MAB) problems in which arms with a Gaussian reward feedback are clustered. Such an arm setting finds applications in many real-world problems, for example, mmWave communications and portfolio...
Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganization in Neural Networks
arXiv:2602.15997v1 Announce Type: new Abstract: Capability emergence during neural network training remains mechanistically opaque. We track five geometric measures across five model scales (405K-85M parameters), 120+ emergence events in eight algorithmic tasks, and three Pythia language models (160M-2.8B). We find:...
MolCrystalFlow: Molecular Crystal Structure Prediction via Flow Matching
arXiv:2602.16020v1 Announce Type: new Abstract: Molecular crystal structure prediction represents a grand challenge in computational chemistry due to large sizes of constituent molecules and complex intra- and intermolecular interactions. While generative modeling has revolutionized structure discovery for molecules, inorganic solids,...
AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
arXiv:2602.16042v1 Announce Type: new Abstract: As machine learning (ML) continues its rapid expansion, the environmental cost of model training and inference has become a critical societal concern. Existing benchmarks overwhelmingly focus on standard performance metrics such as accuracy, BLEU, or...
Extracting and Analyzing Rail Crossing Behavior Signatures from Videos using Tensor Methods
arXiv:2602.16057v1 Announce Type: new Abstract: Railway crossings present complex safety challenges where driver behavior varies by location, time, and conditions. Traditional approaches analyze crossings individually, limiting the ability to identify shared behavioral patterns across locations. We propose a multi-view tensor...
Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training
arXiv:2602.16065v1 Announce Type: new Abstract: Generative Artificial Intelligence (AI), such as large language models (LLMs), has become a transformative force across science, industry, and society. As these systems grow in popularity, web data becomes increasingly interwoven with this AI-generated material...
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
arXiv:2602.16072v1 Announce Type: new Abstract: Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG)....
Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring
arXiv:2602.16101v1 Announce Type: new Abstract: Reliable and cost-effective maintenance is essential for railway safety, particularly at the wheel-rail interface, which is prone to wear and failure. Predictive maintenance frameworks increasingly leverage sensor-generated time-series data, yet traditional methods require manual feature...
On the Power of Source Screening for Learning Shared Feature Extractors
arXiv:2602.16125v1 Announce Type: new Abstract: Learning with shared representation is widely recognized as an effective way to separate commonalities from heterogeneity across various heterogeneous sources. Most existing work includes all related data sources via simultaneously training a common feature extractor...
HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents
arXiv:2602.16165v1 Announce Type: new Abstract: Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and delayed rewards, where agents must execute extended sequences of actions before receiving meaningful feedback. Most existing reinforcement learning...
Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning
arXiv:2602.16167v1 Announce Type: new Abstract: Physics-informed neural networks and neural operators often suffer from severe optimization difficulties caused by ill-conditioned gradients, multi-scale spectral behavior, and stiffness induced by physical constraints. Recently, the Muon optimizer has shown promise by performing orthogonalized...
Towards Secure and Scalable Energy Theft Detection: A Federated Learning Approach for Resource-Constrained Smart Meters
arXiv:2602.16181v1 Announce Type: new Abstract: Energy theft poses a significant threat to the stability and efficiency of smart grids, leading to substantial economic losses and operational challenges. Traditional centralized machine learning approaches for theft detection require aggregating user data, raising...
Deep TPC: Temporal-Prior Conditioning for Time Series Forecasting
arXiv:2602.16188v1 Announce Type: new Abstract: LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely frozen decoder, which limits temporal reasoning as this information degrades through the layers. We introduce...
Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning
arXiv:2602.16196v1 Announce Type: new Abstract: Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the joint state-action space scales exponentially with the number of agents. Mean-field methods alleviate this burden...
ModalImmune: Immunity Driven Unlearning via Self Destructive Training
arXiv:2602.16197v1 Announce Type: new Abstract: Multimodal systems are vulnerable to partial or complete loss of input channels at deployment, which undermines reliability in real-world settings. This paper presents ModalImmune, a training framework that enforces modality immunity by intentionally and controllably...
Linked Data Classification using Neurochaos Learning
arXiv:2602.16204v1 Announce Type: new Abstract: Neurochaos Learning (NL) has shown promise in recent times over traditional deep learning due to its two key features: ability to learn from small sized training samples, and low compute requirements. In prior work, NL...
Geometric Neural Operators via Lie Group-Constrained Latent Dynamics
arXiv:2602.16209v1 Announce Type: new Abstract: Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant and data-driven manner. Existing neural operators, however, often suffer from instability in multi-layer iteration and...
Graph neural network for colliding particles with an application to sea ice floe modeling
arXiv:2602.16213v1 Announce Type: new Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions,...
UCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection
arXiv:2602.16216v1 Announce Type: new Abstract: Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings. This paper proposes UCTECG-Net, an uncertainty-aware hybrid architecture that combines one-dimensional convolutions and Transformer encoders...
Bayesian Quadrature: Gaussian Processes for Integration
arXiv:2602.16218v1 Announce Type: new Abstract: Bayesian quadrature is a probabilistic, model-based approach to numerical integration, the estimation of intractable integrals, or expectations. Although Bayesian quadrature was popularised already in the 1980s, no systematic and comprehensive treatment has been published. The...
Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
arXiv:2602.16224v1 Announce Type: new Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal data distribution, leading to training instability or convergence to poor local minima. Therefore,...
Factored Latent Action World Models
arXiv:2602.16229v1 Announce Type: new Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions provide a natural interface for users to iteratively generate and manipulate videos. However, most...
Online Prediction of Stochastic Sequences with High Probability Regret Bounds
arXiv:2602.16236v1 Announce Type: new Abstract: We revisit the classical problem of universal prediction of stochastic sequences with a finite time horizon $T$ known to the learner. The question we investigate is whether it is possible to derive vanishing regret bounds...
Regret and Sample Complexity of Online Q-Learning via Concentration of Stochastic Approximation with Time-Inhomogeneous Markov Chains
arXiv:2602.16274v1 Announce Type: new Abstract: We present the first high-probability regret bound for classical online Q-learning in infinite-horizon discounted Markov decision processes, without relying on optimism or bonus terms. We first analyze Boltzmann Q-learning with decaying temperature and show that...
Democrats ask Supreme Court not to disrupt New York redistricting dispute
Two separate groups of New York voters and elected officials on Thursday afternoon urged the Supreme Court to leave in place a ruling by a state trial judge in Manhattan […]The postDemocrats ask Supreme Court not to disrupt New York...
Can courts excuse late removals to federal court?
As many law students learn in their civil procedure course, when a plaintiff files suit in state court asserting a claim over which a federal district court would have jurisdiction, […]The postCan courts excuse late removals to federal court?appeared first...
What the Justice Department overlooks in its historical argument to end birthright citizenship
Immigration Matters is a recurring series by César Cuauhtémoc García Hernández that analyzes the court’s immigration docket, highlighting emerging legal questions about new policy and enforcement practices. In my last […]The postWhat the Justice Department overlooks in its historical argument...
SCOTUStoday for Thursday, February 19
Updated on Feb. 19 at 9:50 a.m. President Franklin D. Roosevelt issued Executive Order 9066 on this day in 1942, authorizing the removal of Japanese Americans to internment camps. In […]The postSCOTUStoday for Thursday, February 19appeared first onSCOTUSblog.
“Open & Close Strategy”: How Japanese Tech Companies with Niche Technologies Can Leverage IP for Competitive Advantage
Tomotaka Hosokawa, LL.M. Class of 2026 The Strategy The “Open & Close Strategy” refers to a business and intellectual property strategy where a Japanese technology company intentionally “opens” specific technologies to expand the market while simultaneously “closing” other technologies to...