Gradient-Informed Temporal Sampling Improves Rollout Accuracy in PDE Surrogate Training
arXiv:2603.18237v1 Announce Type: new Abstract: Researchers train neural simulators on uniformly sampled numerical simulation data. But under the same budget, does systematically sampled data provide the most effective information? A fundamental yet unformalized problem is how to sample training data...
AGRI-Fidelity: Evaluating the Reliability of Listenable Explanations for Poultry Disease Detection
arXiv:2603.18247v1 Announce Type: new Abstract: Existing XAI metrics measure faithfulness for a single model, ignoring model multiplicity where near-optimal classifiers rely on different or spurious acoustic cues. In noisy farm environments, stationary artifacts such as ventilation noise can produce explanations...
Detection Is Cheap, Routing Is Learned: Why Refusal-Based Alignment Evaluation Fails
arXiv:2603.18280v1 Announce Type: new Abstract: Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the layer where alignment often operates: routing from concept detection to behavioral policy. We study political censorship...
On Additive Gaussian Processes for Wind Farm Power Prediction
arXiv:2603.18281v1 Announce Type: new Abstract: Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, exploring the use of additive Gaussian processes to reveal variations in turbine-specific and farm-level power...
Path-Constrained Mixture-of-Experts
arXiv:2603.18297v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling by activating only a subset of parameters for each input. However, conventional MoE routing selects each layer's experts independently, creating N^L possible expert paths -- for N experts...
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...
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...
Towards Noise-Resilient Quantum Multi-Armed and Stochastic Linear Bandits
arXiv:2603.18431v1 Announce Type: new Abstract: Quantum multi-armed bandits (MAB) and stochastic linear bandits (SLB) have recently attracted significant attention, as their quantum counterparts can achieve quadratic speedups over classical MAB and SLB. However, most existing quantum MAB algorithms assume ideal...
Seeking Universal Shot Language Understanding Solutions
arXiv:2603.18448v1 Announce Type: new Abstract: Shot language understanding (SLU) is crucial for cinematic analysis but remains challenging due to its diverse cinematographic dimensions and subjective expert judgment. While vision-language models (VLMs) have shown strong ability in general visual understanding, recent...
AIMER: Calibration-Free Task-Agnostic MoE Pruning
arXiv:2603.18492v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token compute, but the deployment still requires storing all experts, making expert pruning important for reducing memory and serving overhead. Existing task-agnostic expert pruning methods are...
Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning
arXiv:2603.18538v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) remains highly vulnerable to adaptive backdoor attacks designed to bypass traditional passive defense metrics. To address this limitation, we shift the defensive paradigm toward a novel active, interventional auditing framework. First,...
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...
Uninjured class members, hindsight harmlessness, presidential cronies, and the mistaken use of deadly force
The Relist Watch column examines cert petitions that the Supreme Court has “relisted” for its upcoming conference. A short explanation of relists is available here. There are 261 petitions and applications […]The postUninjured class members, hindsight harmlessness, presidential cronies, and...
FBI started buying Americans' location data again, Kash Patel confirms
Tom Cotton supports FBI data purchasing, compares it to searching people's trash.
Afroman keeps trolling cops after winning “Lemon Pound Cake” defamation case
Cops asked the jury for millions after Afroman used raid footage in music videos.
Jeff Bezos reportedly wants $100 billion to buy and transform old manufacturing firms with AI
The Amazon magnate has a new project centered around acquiring industrial firms and revamping them with AI technology.
Meta rolls out new AI content enforcement systems while reducing reliance on third-party vendors
Meta believes these AI systems can detect more violations with greater accuracy, better prevent scams, respond more quickly to real-world events, and reduce over-enforcement.
DoorDash launches a new ‘Tasks’ app that pays couriers to submit videos to train AI
Delivery couriers will be able to earn money by completing activities like filming everyday tasks or recording themselves speaking in another language.
TechCrunch Startup Battlefield 200 nominations are still open
Nominate your startup, or one you know, for TechCrunch Startup Battlefield 200 before May 27. Chance to win $100,000 equity-free funding and VC access.
Amazon brings Alexa+ to the UK
The company is currently letting users in the U.K. try out Alexa+ for free via an early access program.
Multiverse Computing pushes its compressed AI models into the mainstream
After compressing models from major AI labs, including OpenAI, Meta, DeepSeek, and Mistral AI, Multiverse Computing has launched both an app that showcases the capabilities of its compressed models and an API that makes them more widely available.
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 State of Charity Care in the United States: Holding Nonprofit Hospitals Accountable for Their Tax Exemptions
Introduction A health system in the Midwest withholds medical care from patients who have $4,500 or more of unpaid debt.[1] A busy university hospital in Manhattan has emergency room nurses redirecting homeless patients to a public hospital that primarily serves...
Catching Pokémon, Not Tax Bills
Introduction What if we told you that you could play a unique and magical game for free? What if we told you this game would let you chase fantastical creatures across your neighborhood, turning your daily stroll into an epic...
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...
MHPO: Modulated Hazard-aware Policy Optimization for Stable Reinforcement Learning
arXiv:2603.16929v1 Announce Type: new Abstract: Regulating the importance ratio is critical for the training stability of Group Relative Policy Optimization (GRPO) based frameworks. However, prevailing ratio control methods, such as hard clipping, suffer from non-differentiable boundaries and vanishing gradient regions,...
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...