Adaptive regularization parameter selection for high-dimensional inverse problems: A Bayesian approach with Tucker low-rank constraints
arXiv:2603.16066v1 Announce Type: new Abstract: This paper introduces a novel variational Bayesian method that integrates Tucker decomposition for efficient high-dimensional inverse problem solving. The method reduces computational complexity by transforming variational inference from a high-dimensional space to a lower-dimensional core...
MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Compute-optimal Scaling of Diffusion Language Models
arXiv:2603.16077v1 Announce Type: new Abstract: Masked diffusion models (MDM) exhibit superior generalization when learned using a Partial masking scheme (Prime). This approach converts tokens into sub-tokens and models the diffusion process at the sub-token level. We identify two limitations of...
A Depth-Aware Comparative Study of Euclidean and Hyperbolic Graph Neural Networks on Bitcoin Transaction Systems
arXiv:2603.16080v1 Announce Type: new Abstract: Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzing such systems, supporting tasks such as...
Functorial Neural Architectures from Higher Inductive Types
arXiv:2603.16123v1 Announce Type: new Abstract: Neural networks systematically fail at compositional generalization -- producing correct outputs for novel combinations of known parts. We show that this failure is architectural: compositional generalization is equivalent to functoriality of the decoder, and this...
Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards
arXiv:2603.16140v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven recent capability advances of large language models across various domains. Recent studies suggest that improved RLVR algorithms allow models to learn effectively from incorrect annotations, achieving performance...
The Supreme Court of Canada
Welcome to SCOTUSblog’s recurring series in which we interview experts on different supreme courts around the world and how they compare to our own. For our debut column, we covered […]The postThe Supreme Court of Canadaappeared first onSCOTUSblog.
The biggest names on the briefs
Empirical SCOTUS is a recurring series by Adam Feldman that looks at Supreme Court data, primarily in the form of opinions and oral arguments, to provide insights into the justices’ decision making and […]The postThe biggest names on the briefsappeared...
SCOTUStoday for Tuesday, March 17
Happy St. Patrick’s Day! We recommend celebrating by reading about Supreme Court justices of Irish descent.The postSCOTUStoday for Tuesday, March 17appeared first onSCOTUSblog.
Apple can delist apps "with or without cause," judge says in loss for Musi app
Judge tosses Musi case against Apple, sanctions lawyers for "mak[ing] up facts."
Trump's plan to shut down weather and climate center triggers lawsuit
Suit: The National Center for Atmospheric Research is to be terminated for no rational reason.
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Formulating Public Pharma
In 2022, prices for both brand-name and generic drugs in the United States were nearly three times as high as prices in comparably industrialized nations, with the cost of insulin products in particular being nearly ten times as high. As...
A Critical Analysis Of Rap Shield Laws
For years, scholars have been sounding the alarm on “rap on trial,” or the use of rap as evidence in criminal proceedings, pointing out that the fundamental characteristics of rap music make it uniquely susceptible to misinterpretation and prejudice. Scholars...
DeceptGuard :A Constitutional Oversight Framework For Detecting Deception in LLM Agents
arXiv:2603.13791v1 Announce Type: new Abstract: Reliable detection of deceptive behavior in Large Language Model (LLM) agents is an essential prerequisite for safe deployment in high-stakes agentic contexts. Prior work on scheming detection has focused exclusively on black-box monitors that observe...
Multi-Axis Trust Modeling for Interpretable Account Hijacking Detection
arXiv:2603.13246v1 Announce Type: new Abstract: This paper proposes a Hadith-inspired multi-axis trust modeling framework, motivated by a structurally analogous problem in classical Hadith scholarship: assessing the trustworthiness of information sources using interpretable, multidimensional criteria rather than a single anomaly score....
DOVA: Deliberation-First Multi-Agent Orchestration for Autonomous Research Automation
arXiv:2603.13327v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated remarkable capabilities in tool use, reasoning, and code generation, yet single-agent systems exhibit fundamental limitations when confronted with complex research tasks demanding multi-source synthesis, adversarial verification, and personalized...
Agent-Based User-Adaptive Filtering for Categorized Harassing Communication
arXiv:2603.13288v1 Announce Type: new Abstract: We propose an agent-based framework for personalized filtering of categorized harassing communication in online social networks. Unlike global moderation systems that apply uniform filtering rules, our approach models user-specific tolerance levels and preferences through adaptive...
Distilling Deep Reinforcement Learning into Interpretable Fuzzy Rules: An Explainable AI Framework
arXiv:2603.13257v1 Announce Type: new Abstract: Deep Reinforcement Learning (DRL) agents achieve remarkable performance in continuous control but remain opaque, hindering deployment in safety-critical domains. Existing explainability methods either provide only local insights (SHAP, LIME) or employ over-simplified surrogates failing to...
The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning
arXiv:2603.13372v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus (ARC-AGI) has become a key benchmark for fluid intelligence in AI. This survey presents the first cross-generation analysis of 82 approaches across three benchmark versions and the ARC Prize 2024-2025...
EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings
arXiv:2603.13594v1 Announce Type: new Abstract: Large language models are shifting from passive information providers to active agents intended for complex workflows. However, their deployment as reliable AI workers in enterprise is stalled by benchmarks that fail to capture the intricacies...
From Refusal Tokens to Refusal Control: Discovering and Steering Category-Specific Refusal Directions
arXiv:2603.13359v1 Announce Type: new Abstract: Language models are commonly fine-tuned for safety alignment to refuse harmful prompts. One approach fine-tunes them to generate categorical refusal tokens that distinguish different refusal types before responding. In this work, we leverage a version...
DyACE: Dynamic Algorithm Co-evolution for Online Automated Heuristic Design with Large Language Model
arXiv:2603.13344v1 Announce Type: new Abstract: The prevailing paradigm in Automated Heuristic Design (AHD) typically relies on the assumption that a single, fixed algorithm can effectively navigate the shifting dynamics of a combinatorial search. This static approach often proves inadequate for...
MESD: Detecting and Mitigating Procedural Bias in Intersectional Groups
arXiv:2603.13452v1 Announce Type: new Abstract: Research about bias in machine learning has mostly focused on outcome-oriented fairness metrics (e.g., equalized odds) and on a single protected category. Although these approaches offer great insight into bias in ML, they provide limited...
LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes
arXiv:2603.13673v1 Announce Type: new Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging. However, this information is usually embedded in unstructured textual data rather than...
Knowledge Distillation for Large Language Models
arXiv:2603.13765v1 Announce Type: new Abstract: We propose a resource-efficient framework for compressing large language models through knowledge distillation, combined with guided chain-of-thought reinforcement learning. Using Qwen 3B as the teacher and Qwen 0.5B as the student, we apply knowledge distillation...
Deep Convolutional Architectures for EEG Classification: A Comparative Study with Temporal Augmentation and Confidence-Based Voting
arXiv:2603.13261v1 Announce Type: new Abstract: Electroencephalography (EEG) classification plays a key role in brain-computer interface (BCI) systems, yet it remains challenging due to the low signal-to-noise ratio, temporal variability of neural responses, and limited data availability. In this paper, we...
Can We Trust LLMs on Memristors? Diving into Reasoning Ability under Non-Ideality
arXiv:2603.13725v1 Announce Type: new Abstract: Memristor-based analog compute-in-memory (CIM) architectures provide a promising substrate for the efficient deployment of Large Language Models (LLMs), owing to superior energy efficiency and computational density. However, these architectures suffer from precision issues caused by...
Multimodal Emotion Regression with Multi-Objective Optimization and VAD-Aware Audio Modeling for the 10th ABAW EMI Track
arXiv:2603.13760v1 Announce Type: new Abstract: We participated in the 10th ABAW Challenge, focusing on the Emotional Mimicry Intensity (EMI) Estimation track on the Hume-Vidmimic2 dataset. This task aims to predict six continuous emotion dimensions: Admiration, Amusement, Determination, Empathic Pain, Excitement,...
The AI Fiction Paradox
arXiv:2603.13545v1 Announce Type: new Abstract: AI development has a fiction dependency problem: models are built on massive corpora of modern fiction and desperately need more of it, yet they struggle to generate it. I term this the AI-Fiction Paradox and...
PA-Net: Precipitation-Adaptive Mixture-of-Experts for Long-Tail Rainfall Nowcasting
arXiv:2603.13818v1 Announce Type: new Abstract: Precipitation nowcasting is vital for flood warning, agricultural management, and emergency response, yet two bottlenecks persist: the prohibitive cost of modeling million-scale spatiotemporal tokens from multi-variate atmospheric fields, and the extreme long-tailed rainfall distribution where...