Escaping Offline Pessimism: Vector-Field Reward Shaping for Safe Frontier Exploration
arXiv:2603.18326v1 Announce Type: new Abstract: While offline reinforcement learning provides reliable policies for real-world deployment, its inherent pessimism severely restricts an agent's ability to explore and collect novel data online. Drawing inspiration from safe reinforcement learning, exploring near the boundary...
A Family of Adaptive Activation Functions for Mitigating Failure Modes in Physics-Informed Neural Networks
arXiv:2603.18328v1 Announce Type: new Abstract: Physics-Informed Neural Networks(PINNs) are a powerful and flexible learning framework that has gained significant attention in recent years. It has demonstrated strong performance across a wide range of scientific and engineering problems. In parallel, wavelets...
Epistemic Generative Adversarial Networks
arXiv:2603.18348v1 Announce Type: new Abstract: Generative models, particularly Generative Adversarial Networks (GANs), often suffer from a lack of output diversity, frequently generating similar samples rather than a wide range of variations. This paper introduces a novel generalization of the GAN...
Discounted Beta--Bernoulli Reward Estimation for Sample-Efficient Reinforcement Learning with Verifiable Rewards
arXiv:2603.18444v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for improving the reasoning capabilities of large language models. However, existing group-based RLVR methods often suffer from severe sample inefficiency. This inefficiency...
AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models
arXiv:2603.18464v1 Announce Type: new Abstract: Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models faces significant challenges in computational efficiency and data acquisition. We propose AcceRL, a fully asynchronous and decoupled RL framework designed to eliminate synchronization barriers by physically isolating...
Birthright citizenship: why the text, history, and structure of a landmark 1952 statute doom Trump’s executive order
Brothers in Law is a recurring series by brothers Akhil and Vikram Amar, with special emphasis on measuring what the Supreme Court says against what the Constitution itself says. For more content from […]The postBirthright citizenship: why the text, history,...
Justices to consider rules pardoning omissions by bankrupt debtors
Next week’s argument in Keathley v. Buddy Ayers Construction involves a technical question about bankruptcy procedure – the standards for overlooking the failure of a debtor in bankruptcy to mention […]The postJustices to consider rules pardoning omissions by bankrupt debtorsappeared...
Volume 2026, No. 1 – Wisconsin Law Review – UW–Madison
Contract Law and Civil Justice in Local Courts by Cathy Hwang & Justin Weinstein-Tull; Preempting Drug Price Reform by Shweta Kumar; Lessons Learned? COVID’s Continued Impact on Remote Work Disability Accommodations by D’Andra Millsap Shu; Unbundling AI Openness by Parth...
The Singular Role of Public Pension Funds in Corporate Governance
Introduction U.S. public pension funds manage more than $6 trillion in assets.[1] The law, policy, and public debates about how they should manage this money are based on a theoretical model that is descriptively inaccurate and yields policy proposals that...
Applying History as Law: The Role of Historical Facts in Implementing Constitutional Doctrine
Introduction The relevance of historical facts to constitutional law has never been greater or more contested in our legal system. In an increasingly wide range of cases involving everything from abortion[1] and gun rights[2] to trademark law[3] and agency funding,[4]...
A foundation model for electrodermal activity data
arXiv:2603.16878v1 Announce Type: new Abstract: Foundation models have recently extended beyond natural language and vision to timeseries domains, including physiological signals. However, progress in electrodermal activity (EDA) modeling is hindered by the absence of large-scale, curated, and openly accessible datasets....
HoloByte: Continuous Hyperspherical Distillation for Tokenizer-Free Modeling
arXiv:2603.16917v1 Announce Type: new Abstract: Sequence modeling universally relies on discrete subword tokenization to circumvent the $\mathcal{O}(N^2)$ computational intractability of native byte-level attention. However, this heuristic quantization imposes artificial morphological boundaries, enforces vocabulary dependence, and fractures the continuity of the...
Minimum-Action Learning: Energy-Constrained Symbolic Model Selection for Physical Law Identification from Noisy Data
arXiv:2603.16951v1 Announce Type: new Abstract: Identifying physical laws from noisy observational data is a central challenge in scientific machine learning. We present Minimum-Action Learning (MAL), a framework that selects symbolic force laws from a pre-specified basis library by minimizing a...
Formal verification of tree-based machine learning models for lateral spreading
arXiv:2603.16983v1 Announce Type: new Abstract: Machine learning models for geotechnical hazard prediction can achieve high accuracy while learning physically inconsistent relationships from sparse or biased training data. Current remedies (post-hoc explainability, such as SHAP and LIME, and training-time constraints) either...
Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting
arXiv:2603.16985v1 Announce Type: new Abstract: Transformer-based models have been widely adopted for time-series forecasting due to their high representational capacity and architectural flexibility. However, many Transformer variants implicitly assume stationarity and stable temporal dynamics -- assumptions routinely violated in financial...
Do Understanding and Generation Fight? A Diagnostic Study of DPO for Unified Multimodal Models
arXiv:2603.17044v1 Announce Type: new Abstract: Unified multimodal models share a language model backbone for both understanding and generating images. Can DPO align both capabilities simultaneously? We present the first systematic study of this question, applying DPO to Janus-Pro at 1B...
PRISM: Demystifying Retention and Interaction in Mid-Training
arXiv:2603.17074v1 Announce Type: new Abstract: We present PRISM, a comprehensive empirical study of mid-training design choices for large language models. Through controlled experiments across seven base models spanning four families (Granite, LLaMA, Mistral, Nemotron-H), two architecture types (dense Transformer and...
CircuitBuilder: From Polynomials to Circuits via Reinforcement Learning
arXiv:2603.17075v1 Announce Type: new Abstract: Motivated by auto-proof generation and Valiant's VP vs. VNP conjecture, we study the problem of discovering efficient arithmetic circuits to compute polynomials, using addition and multiplication gates. We formulate this problem as a single-player game,...
SENSE: Efficient EEG-to-Text via Privacy-Preserving Semantic Retrieval
arXiv:2603.17109v1 Announce Type: new Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction. Most existing Brain-Computer Interface (BCI) approaches rely on memory-intensive fine-tuning of Large Language...
Topology-Preserving Deep Joint Source-Channel Coding for Semantic Communication
arXiv:2603.17126v1 Announce Type: new Abstract: Many wireless vision applications, such as autonomous driving, require preservation of global structural information rather than only per-pixel fidelity. However, existing Deep joint source-channel coding (DeepJSCC) schemes mainly optimize pixel-wise losses and provide no explicit...
Contextual Preference Distribution Learning
arXiv:2603.17139v1 Announce Type: new Abstract: Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse...
Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
arXiv:2603.17148v1 Announce Type: new Abstract: Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This...
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
arXiv:2603.17175v1 Announce Type: new Abstract: Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can learn non-physical relationships that reduce their reliability in natural hazard applications. This study presents a...
Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probing
arXiv:2603.17199v1 Announce Type: new Abstract: Large language models (LLMs) can produce chains of thought (CoT) that do not accurately reflect the actual factors driving their answers. In multiple-choice settings with an injected hint favoring a particular option, models may shift...
On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings
arXiv:2603.17246v1 Announce Type: new Abstract: Vision-Language Models (VLMs) exhibit a characteristic "cone effect" in which nonlinear encoders map embeddings into highly concentrated regions of the representation space, contributing to cross-modal separation known as the modality gap. While this phenomenon has...
Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction
arXiv:2603.17248v1 Announce Type: new Abstract: Reconstructing a 12-lead electrocardiogram (ECG) from a reduced lead set is an ill-posed inverse problem due to anatomical variability. Standard deep learning methods often ignore underlying cardiac pathology losing vital morphology in precordial leads. We...
Classifier Pooling for Modern Ordinal Classification
arXiv:2603.17278v1 Announce Type: new Abstract: Ordinal data is widely prevalent in clinical and other domains, yet there is a lack of both modern, machine-learning based methods and publicly available software to address it. In this paper, we present a model-agnostic...
Variational Kernel Design for Internal Noise: Gaussian Chaos Noise, Representation Compatibility, and Reliable Deep Learning
arXiv:2603.17365v1 Announce Type: new Abstract: Internal noise in deep networks is usually inherited from heuristics such as dropout, hard masking, or additive perturbation. We ask two questions: what correlation geometry should internal noise have, and is the implemented perturbation compatible...
Cohomological Obstructions to Global Counterfactuals: A Sheaf-Theoretic Foundation for Generative Causal Models
arXiv:2603.17384v1 Announce Type: new Abstract: Current continuous generative models (e.g., Diffusion Models, Flow Matching) implicitly assume that locally consistent causal mechanisms naturally yield globally coherent counterfactuals. In this paper, we prove that this assumption fails fundamentally when the causal graph...