Neural Paging: Learning Context Management Policies for Turing-Complete Agents
arXiv:2603.02228v1 Announce Type: new Abstract: The proof that Large Language Models (LLMs) augmented with external read-write memory constitute a computationally universal system has established the theoretical foundation for general-purpose agents. However, existing implementations face a critical bottleneck: the finite and...
Generalized Discrete Diffusion with Self-Correction
arXiv:2603.02230v1 Announce Type: new Abstract: Self-correction is an effective technique for maintaining parallel sampling in discrete diffusion models with minimal performance degradation. Prior work has explored self-correction at inference time or during post-training; however, such approaches often suffer from limited...
Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
arXiv:2603.02231v1 Announce Type: new Abstract: Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite Element Method (FEM) provide high accuracy but struggle with large-scale or high-frequency problems due...
Beyond Binary Preferences: A Principled Framework for Reward Modeling with Ordinal Feedback
arXiv:2603.02232v1 Announce Type: new Abstract: Reward modeling is crucial for aligning large language models with human preferences, yet current approaches lack a principled mathematical framework for leveraging ordinal preference data. When human annotators provide graded preferences on a Likert scale...
Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
arXiv:2603.02233v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) enables a collection of agents to collaboratively learn individual models without sharing raw data. We propose a new PFL approach in which each agent optimizes a weighted combination of all agents'...
Talking with Verifiers: Automatic Specification Generation for Neural Network Verification
arXiv:2603.02235v1 Announce Type: new Abstract: Neural network verification tools currently support only a narrow class of specifications, typically expressed as low-level constraints over raw inputs and outputs. This limitation significantly hinders their adoption and practical applicability across diverse application domains...
Length Generalization Bounds for Transformers
arXiv:2603.02238v1 Announce Type: new Abstract: Length generalization is a key property of a learning algorithm that enables it to make correct predictions on inputs of any length, given finite training data. To provide such a guarantee, one needs to be...
High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach
arXiv:2603.02265v1 Announce Type: new Abstract: In order to evaluate the invulnerability of networks against various types of attacks and provide guidance for potential performance enhancement as well as controllability maintenance, network controllability robustness (NCR) has attracted increasing attention in recent...
Boosting Meta-Learning for Few-Shot Text Classification via Label-guided Distance Scaling
arXiv:2603.02267v1 Announce Type: new Abstract: Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithms in the training stage. However, the labeled samples are randomly selected during...
PRISM: Exploring Heterogeneous Pretrained EEG Foundation Model Transfer to Clinical Differential Diagnosis
arXiv:2603.02268v1 Announce Type: new Abstract: EEG foundation models are typically pretrained on narrow-source clinical archives and evaluated on benchmarks from the same ecosystem, leaving unclear whether representations encode neural physiology or recording-distribution artifacts. We introduce PRISM (Population Representative Invariant Signal...
Graph Attention Based Prioritization of Disease Responsible Genes from Multimodal Alzheimer's Network
arXiv:2603.02273v1 Announce Type: new Abstract: Prioritizing disease-associated genes is central to understanding the molecular mechanisms of complex disorders such as Alzheimer's disease (AD). Traditional network-based approaches rely on static centrality measures and often fail to capture cross-modal biological heterogeneity. We...
A Comparative Study of UMAP and Other Dimensionality Reduction Methods
arXiv:2603.02275v1 Announce Type: new Abstract: Uniform Manifold Approximation and Projection (UMAP) is a widely used manifold learning technique for dimensionality reduction. This paper studies UMAP, supervised UMAP, and several competing dimensionality reduction methods, including Principal Component Analysis (PCA), Kernel PCA,...
Temporal Imbalance of Positive and Negative Supervision in Class-Incremental Learning
arXiv:2603.02280v1 Announce Type: new Abstract: With the widespread adoption of deep learning in visual tasks, Class-Incremental Learning (CIL) has become an important paradigm for handling dynamically evolving data distributions. However, CIL faces the core challenge of catastrophic forgetting, often manifested...
The Malignant Tail: Spectral Segregation of Label Noise in Over-Parameterized Networks
arXiv:2603.02293v1 Announce Type: new Abstract: While implicit regularization facilitates benign overfitting in low-noise regimes, recent theoretical work predicts a sharp phase transition to harmful overfitting as the noise-to-signal ratio increases. We experimentally isolate the geometric mechanism of this transition: the...
Preconditioned Score and Flow Matching
arXiv:2603.02337v1 Announce Type: new Abstract: Flow matching and score-based diffusion train vector fields under intermediate distributions $p_t$, whose geometry can strongly affect their optimization. We show that the covariance $\Sigma_t$ of $p_t$ governs optimization bias: when $\Sigma_t$ is ill-conditioned, and...
Learning graph topology from metapopulation epidemic encoder-decoder
arXiv:2603.02349v1 Announce Type: new Abstract: Metapopulation epidemic models are a valuable tool for studying large-scale outbreaks. With the limited availability of epidemic tracing data, it is challenging to infer the essential constituents of these models, namely, the epidemic parameters and...
Learning Optimal Search Strategies
arXiv:2603.02356v1 Announce Type: new Abstract: We explore the question of how to learn an optimal search strategy within the example of a parking problem where parking opportunities arrive according to an unknown inhomogeneous Poisson process. The optimal policy is a...
Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles
arXiv:2603.02406v1 Announce Type: new Abstract: Generative models have recently advanced $\textit{de novo}$ protein design by learning the statistical regularities of natural structures. However, current approaches face three key limitations: (1) Existing methods cannot jointly learn protein geometry and design tasks,...
Personalized Multi-Agent Average Reward TD-Learning via Joint Linear Approximation
arXiv:2603.02426v1 Announce Type: new Abstract: We study personalized multi-agent average reward TD learning, in which a collection of agents interacts with different environments and jointly learns their respective value functions. We focus on the setting where there exists a shared...
A Unified Revisit of Temperature in Classification-Based Knowledge Distillation
arXiv:2603.02430v1 Announce Type: new Abstract: A central idea of knowledge distillation is to expose relational structure embedded in the teacher's weights for the student to learn, which is often facilitated using a temperature parameter. Despite its widespread use, there remains...
Manifold Aware Denoising Score Matching (MAD)
arXiv:2603.02452v1 Announce Type: new Abstract: A major focus in designing methods for learning distributions defined on manifolds is to alleviate the need to implicitly learn the manifold so that learning can concentrate on the data distribution within the manifold. However,...
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
arXiv:2603.02462v1 Announce Type: new Abstract: A key challenge in deriving unified neural solvers for combinatorial optimization (CO) is efficient generalization of models between a given set of tasks to new tasks not used during the initial training process. To address...
ParEVO: Synthesizing Code for Irregular Data: High-Performance Parallelism through Agentic Evolution
arXiv:2603.02510v1 Announce Type: new Abstract: The transition from sequential to parallel computing is essential for modern high-performance applications but is hindered by the steep learning curve of concurrent programming. This challenge is magnified for irregular data structures (such as sparse...
Thermodynamic Regulation of Finite-Time Gibbs Training in Energy-Based Models: A Restricted Boltzmann Machine Study
arXiv:2603.02525v1 Announce Type: new Abstract: Restricted Boltzmann Machines (RBMs) are typically trained using finite-length Gibbs chains under a fixed sampling temperature. This practice implicitly assumes that the stochastic regime remains valid as the energy landscape evolves during learning. We argue...
Bridging Diffusion Guidance and Anderson Acceleration via Hopfield Dynamics
arXiv:2603.02531v1 Announce Type: new Abstract: Classifier-Free Guidance (CFG) has significantly enhanced the generative quality of diffusion models by extrapolating between conditional and unconditional outputs. However, its high inference cost and limited applicability to distilled or single-step models have shifted research...
EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networks
arXiv:2603.02562v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing data processing methodologies. However, FL systems face significant communication bottlenecks due to inevitable client-server data...
Court unanimously sides with government in immigration dispute
The Supreme Court unanimously sided with the federal government on Wednesday in Urias-Orellana v. Bondi, holding in an opinion by Justice Ketanji Brown Jackson that federal courts of appeals must […]The postCourt unanimously sides with government in immigration disputeappeared first...
Opinions for Wednesday, March 4
We were live as the court released its opinions in Urias-Orellana v. Bondi and Galette v. New Jersey Transit Corp..The postOpinions for Wednesday, March 4appeared first onSCOTUSblog.