Whatever Did Happen to the Antitrust Movement?
ARTICLE Whatever Did Happen to the Antitrust Movement? Herbert Hovenkamp* Antitrust in the United States today is caught between its pursuit of technical rules designed to define and implement defensible economic goals, and increasingly political calls for a new antitrust...
Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks
arXiv:2603.16881v1 Announce Type: new Abstract: Multi-agent deep learning (MADL), including multi-agent deep reinforcement learning (MADRL), distributed/federated training, and graph-structured neural networks, is becoming a unifying framework for decision-making and inference in wireless systems where sensing, communication, and computing are tightly...
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...
Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching
arXiv:2603.17403v1 Announce Type: new Abstract: Earthquake hazard analysis and design of spatially distributed infrastructure, such as power grids and energy pipeline networks, require scenario-specific ground-motion time histories with realistic frequency content and spatiotemporal coherence. However, producing the large ensembles needed...
DOD says Anthropic’s ‘red lines’ make it an ‘unacceptable risk to national security’
The Defense Department said concerns that Anthropic might "attempt to disable its technology" during "warfighting operations" validate its decision to label the AI firm a supply-chain risk.
Proactive Rejection and Grounded Execution: A Dual-Stage Intent Analysis Paradigm for Safe and Efficient AIoT Smart Homes
arXiv:2603.16207v1 Announce Type: new Abstract: As Large Language Models (LLMs) transition from information providers to embodied agents in the Internet of Things (IoT), they face significant challenges regarding reliability and interaction efficiency. Direct execution of LLM-generated commands often leads to...
Persona-Conditioned Risk Behavior in Large Language Models: A Simulated Gambling Study with GPT-4.1
arXiv:2603.15831v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in uncertain, sequential decision-making contexts. Yet it remains poorly understood whether the behaviors they exhibit in such environments reflect principled cognitive patterns or simply surface-level...
Understanding Moral Reasoning Trajectories in Large Language Models: Toward Probing-Based Explainability
arXiv:2603.16017v1 Announce Type: new Abstract: Large language models (LLMs) increasingly participate in morally sensitive decision-making, yet how they organize ethical frameworks across reasoning steps remains underexplored. We introduce \textit{moral reasoning trajectories}, sequences of ethical framework invocations across intermediate reasoning steps,...
CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering
arXiv:2603.16091v1 Announce Type: new Abstract: In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet still settles on the wrong answer. We present CounterRefine, a lightweight inference-time repair layer...
Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users
arXiv:2603.16120v1 Announce Type: new Abstract: Deep Research (DR) tools (e.g. OpenAI DR) help researchers cope with ballooning publishing counts. Such tools can synthesize scientific papers to answer researchers' queries, but lack understanding of their users. We change that in MyScholarQA...
SIA: A Synthesize-Inject-Align Framework for Knowledge-Grounded and Secure E-commerce Search LLMs with Industrial Deployment
arXiv:2603.16137v1 Announce Type: new Abstract: Large language models offer transformative potential for e-commerce search by enabling intent-aware recommendations. However, their industrial deployment is hindered by two critical challenges: (1) knowledge hallucination due to insufficient encoding of dynamic, fine-grained product knowledge,...
DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning
arXiv:2603.16459v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have emerged as a promising alternative to auto-regressive models due to their iterative refinement capabilities. However, hallucinations remain a critical issue that hinders their reliability. To detect hallucination responses from...
Auto Researching, not hyperparameter tuning: Convergence Analysis of 10,000 Experiments
arXiv:2603.15916v1 Announce Type: new Abstract: When LLM agents autonomously design ML experiments, do they perform genuine architecture search -- or do they default to hyperparameter tuning within a narrow region of the design space? We answer this question by analyzing...
Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems
arXiv:2603.15927v1 Announce Type: new Abstract: We propose a data-driven framework to learn interaction kernels in stochastic multi-agent systems. Our approach aims at identifying the functional form of nonlocal interaction and diffusion terms directly from trajectory data, without any a priori...
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...
How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing
arXiv:2603.13259v1 Announce Type: new Abstract: When a language model is fed a wrong answer, what happens inside the network? Current understanding treats truthfulness as a static property of individual-layer representations-a direction to be probed, a feature to be extracted. Less...
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...
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....
Knowledge, Rules and Their Embeddings: Two Paths towards Neuro-Symbolic JEPA
arXiv:2603.13265v1 Announce Type: new Abstract: Modern self-supervised predictive architectures excel at capturing complex statistical correlations from high-dimensional data but lack mechanisms to internalize verifiable human logic, leaving them susceptible to spurious correlations and shortcut learning. Conversely, traditional rule-based inference systems...
ICPRL: Acquiring Physical Intuition from Interactive Control
arXiv:2603.13295v1 Announce Type: new Abstract: VLMs excel at static perception but falter in interactive reasoning in dynamic physical environments, which demands planning and adaptation to dynamic outcomes. Existing physical reasoning methods often depend on abstract symbolic inputs or lack the...
Feature-level Interaction Explanations in Multimodal Transformers
arXiv:2603.13326v1 Announce Type: new Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multimodal explainable AI (MXAI) methods extend unimodal saliency to multimodal backbones, highlighting important tokens or patches within each modality,...
RBF-Solver: A Multistep Sampler for Diffusion Probabilistic Models via Radial Basis Functions
arXiv:2603.13330v1 Announce Type: new Abstract: Diffusion probabilistic models (DPMs) are widely adopted for their outstanding generative fidelity, yet their sampling is computationally demanding. Polynomial-based multistep samplers mitigate this cost by accelerating inference; however, despite their theoretical accuracy guarantees, they generate...
Lipschitz-Based Robustness Certification Under Floating-Point Execution
arXiv:2603.13334v1 Announce Type: new Abstract: Sensitivity-based robustness certification has emerged as a practical approach for certifying neural network robustness, including in settings that require verifiable guarantees. A key advantage of these methods is that certification is performed by concrete numerical...
Justices will hear argument on Trump administration’s removal of protected status for Syrian and Haitian nationals
The Supreme Court announced on Monday afternoon that it will hear oral argument on whether the Trump administration can end a program that allows several thousand Syrians and approximately 350,000 […]The postJustices will hear argument on Trump administration’s removal of...
Haitian nationals ask court to deny Trump administration’s request to remove their protected status
A group of Haitian nationals urged the Supreme Court on Monday to leave in place a ruling by a federal judge in Washington, D.C., that allows them to stay in […]The postHaitian nationals ask court to deny Trump administration’s request...
Trump and his FCC chair demand more positive news coverage of Iran war
Carr makes evidence-free claim of "hoaxes and news distortions." Trump is thrilled.
Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
arXiv:2603.12933v1 Announce Type: new Abstract: Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the quality--cost trade-off space. Despite these advances, real-world deployment is often constrained...
Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel
arXiv:2603.12483v1 Announce Type: new Abstract: Across many domains (e.g., IoT, observability, telecommunications, cybersecurity), there is an emerging adoption of conversational data analysis agents that enable users to "talk to your data" to extract insights. Such data analysis agents operate on...
Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations
arXiv:2603.12813v1 Announce Type: new Abstract: Agentic AI systems integrating large language models (LLMs) with reasoning and tooluse capabilities are transforming various domains - in particular, software development. In contrast, their application in chemical process flowsheet modelling remains largely unexplored. In...
Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
arXiv:2603.12349v1 Announce Type: cross Abstract: Scientific discovery increasingly relies on AI systems to select candidates for expensive experimental validation, yet no principled, budget-aware evaluation framework exists for comparing selection strategies -- a gap intensified by large language models (LLMs), which...