Towards Explainable Deep Learning for Ship Trajectory Prediction in Inland Waterways
arXiv:2603.04472v1 Announce Type: new Abstract: Accurate predictions of ship trajectories in crowded environments are essential to ensure safety in inland waterways traffic. Recent advances in deep learning promise increased accuracy even for complex scenarios. While the challenge of ship-to-ship awareness...
Standing on the Shoulders of Giants: Rethinking EEG Foundation Model Pretraining via Multi-Teacher Distillation
arXiv:2603.04478v1 Announce Type: new Abstract: Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely adapted from and inspired by the success of vision and language foundation models. However, unlike images and text, EEG...
Augmenting representations with scientific papers
arXiv:2603.04516v1 Announce Type: new Abstract: Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical sources. Still, these data sources are rarely systematically integrated. This work introduces a...
Oracle-efficient Hybrid Learning with Constrained Adversaries
arXiv:2603.04546v1 Announce Type: new Abstract: The Hybrid Online Learning Problem, where features are drawn i.i.d. from an unknown distribution but labels are generated adversarially, is a well-motivated setting positioned between statistical and fully-adversarial online learning. Prior work has presented a...
Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
arXiv:2603.04553v1 Announce Type: new Abstract: We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM autonomously discovers keypoints, bounding boxes, and object masks directly from video data, enabling...
Why Do Neural Networks Forget: A Study of Collapse in Continual Learning
arXiv:2603.04580v1 Announce Type: new Abstract: Catastrophic forgetting is a major problem in continual learning, and lots of approaches arise to reduce it. However, most of them are evaluated through task accuracy, which ignores the internal model structure. Recent research suggests...
A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments
arXiv:2603.04595v1 Announce Type: new Abstract: Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely heavily on direct identifiers such as names, emails,...
K-Means as a Radial Basis function Network: a Variational and Gradient-based Equivalence
arXiv:2603.04625v1 Announce Type: new Abstract: This work establishes a rigorous variational and gradient-based equivalence between the classical K-Means algorithm and differentiable Radial Basis Function (RBF) neural networks with smooth responsibilities. By reparameterizing the K-Means objective and embedding its distortion functional...
When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
arXiv:2603.04648v1 Announce Type: new Abstract: Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and noise-free states. We study robustness of Proximal Policy Optimization (PPO) under temporally persistent...
Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data
arXiv:2603.04683v1 Announce Type: new Abstract: Accurate estimation of forest biomass is crucial for monitoring carbon sequestration and informing climate change mitigation strategies. Existing methods often rely on allometric models, which estimate individual tree biomass by relating it to measurable biophysical...
Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddings
arXiv:2603.04692v1 Announce Type: new Abstract: Predictive modeling in engineering applications has long been dominated by bespoke models and small, siloed tabular datasets, limiting the applicability of large-scale learning approaches. Despite recent progress in tabular foundation models, the resulting synthetic training...
Implicit Bias and Loss of Plasticity in Matrix Completion: Depth Promotes Low-Rankness
arXiv:2603.04703v1 Announce Type: new Abstract: We study matrix completion via deep matrix factorization (a.k.a. deep linear neural networks) as a simplified testbed to examine how network depth influences training dynamics. Despite the simplicity and importance of the problem, prior theory...
Probabilistic Dreaming for World Models
arXiv:2603.04715v1 Announce Type: new Abstract: "Dreaming" enables agents to learn from imagined experiences, enabling more robust and sample-efficient learning of world models. In this work, we consider innovations to the state-of-the-art Dreamer model using probabilistic methods that enable: (1) the...
Count Bridges enable Modeling and Deconvolving Transcriptomic Data
arXiv:2603.04730v1 Announce Type: new Abstract: Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired resolution: while the unit of interest is typically a single...
When Priors Backfire: On the Vulnerability of Unlearnable Examples to Pretraining
arXiv:2603.04731v1 Announce Type: new Abstract: Unlearnable Examples (UEs) serve as a data protection strategy that generates imperceptible perturbations to mislead models into learning spurious correlations instead of underlying semantics. In this paper, we uncover a fundamental vulnerability of UEs that...
Distribution-Conditioned Transport
arXiv:2603.04736v1 Announce Type: new Abstract: Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific applications increasingly require models that can generalize to source and target distributions unseen...
ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation
arXiv:2603.04767v1 Announce Type: new Abstract: Conditional time series generation plays a critical role in addressing data scarcity and enabling causal analysis in real-world applications. Despite its increasing importance, the field lacks a standardized and systematic benchmarking framework for evaluating generative...
Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
arXiv:2603.04768v1 Announce Type: new Abstract: Equalizer parameter optimization is critical for signal integrity in high-speed memory systems operating at multi-gigabit data rates. However, existing methods suffer from computationally expensive eye diagram evaluation, optimization of expected rather than worst-case performance, and...
Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
arXiv:2603.04780v1 Announce Type: new Abstract: Causal discovery with latent variables is a fundamental task. Yet most existing methods rely on strong structural assumptions, such as enforcing specific indicator patterns for latents or restricting how they can interact with others. We...
Diffusion Policy through Conditional Proximal Policy Optimization
arXiv:2603.04790v1 Announce Type: new Abstract: Reinforcement learning (RL) has been extensively employed in a wide range of decision-making problems, such as games and robotics. Recently, diffusion policies have shown strong potential in modeling multi-modal behaviors, enabling more diverse and flexible...
Multilevel Training for Kolmogorov Arnold Networks
arXiv:2603.04827v1 Announce Type: new Abstract: Algorithmic speedup of training common neural architectures is made difficult by the lack of structure guaranteed by the function compositions inherent to such networks. In contrast to multilayer perceptrons (MLPs), Kolmogorov-Arnold networks (KANs) provide more...
Missingness Bias Calibration in Feature Attribution Explanations
arXiv:2603.04831v1 Announce Type: new Abstract: Popular explanation methods often produce unreliable feature importance scores due to missingness bias, a systematic distortion that arises when models are probed with ablated, out-of-distribution inputs. Existing solutions treat this as a deep representational flaw...
Why Is RLHF Alignment Shallow? A Gradient Analysis
arXiv:2603.04851v1 Announce Type: new Abstract: Why is safety alignment in LLMs shallow? We prove that gradient-based alignment inherently concentrates on positions where harm is decided and vanishes beyond. Using a martingale decomposition of sequence-level harm, we derive an exact characterization...
Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness
arXiv:2603.04881v1 Announce Type: new Abstract: Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade performance, introduce fairness issues like disparate impact, and reduce adversarial robustness. The theoretical underpinnings of...
FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation
arXiv:2603.04890v1 Announce Type: new Abstract: Multimodal Federated Learning (MFL) enables clients with heterogeneous data modalities to collaboratively train models without sharing raw data, offering a privacy-preserving framework that leverages complementary cross-modal information. However, existing methods often overlook personalized client performance...
BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning
arXiv:2603.04918v1 Announce Type: new Abstract: Proximal constraints are fundamental to the stability of the Large Language Model reinforcement learning. While the canonical clipping mechanism in PPO serves as an efficient surrogate for trust regions, we identify a critical bottleneck: fixed...
Generative AI in legal education: a two-year experiment with ChatGPT
Justices poised to adopt exceptions to federal criminal defendants’ appellate waivers
The Supreme Court heard oral argument on Tuesday in Hunter v. United States about what exceptions exist to federal defendants’ waivers of their right to appeal. The justices seemed poised […]The postJustices poised to adopt exceptions to federal criminal defendants’...
Birthright citizenship: the exceptions provide the rule
The battle over birthright citizenship is a battle over its exceptions. The 14th Amendment’s first sentence proudly proclaims that “[a]ll persons born . . . in the United States, and subject to the jurisdiction […]The postBirthright citizenship: the exceptions provide...
The emergency docket’s critics have it backwards
Ratio Decidendi is a recurring series by Stephanie Barclay exploring the reasoning – from practical considerations to deep theory – behind our nation’s most consequential constitutional decisions. Last Monday, the […]The postThe emergency docket’s critics have it backwardsappeared first onSCOTUSblog.