Wearable Foundation Models Should Go Beyond Static Encoders
arXiv:2603.19564v1 Announce Type: new Abstract: Wearable foundation models (WFMs), trained on large volumes of data collected by affordable, always-on devices, have demonstrated strong performance on short-term, well-defined health monitoring tasks, including activity recognition, fitness tracking, and cardiovascular signal assessment. However,...
ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization
arXiv:2603.19594v1 Announce Type: new Abstract: Indoor localization has become increasingly essential for applications ranging from asset tracking to delivering personalized services. Federated learning (FL) offers a privacy-preserving approach by training a centralized global model (GM) using distributed data from mobile...
Demonstrations, CoT, and Prompting: A Theoretical Analysis of ICL
arXiv:2603.19611v1 Announce Type: new Abstract: In-Context Learning (ICL) enables pretrained LLMs to adapt to downstream tasks by conditioning on a small set of input-output demonstrations, without any parameter updates. Although there have been many theoretical efforts to explain how ICL...
On Performance Guarantees for Federated Learning with Personalized Constraints
arXiv:2603.19617v1 Announce Type: new Abstract: Federated learning (FL) has emerged as a communication-efficient algorithmic framework for distributed learning across multiple agents. While standard FL formulations capture unconstrained or globally constrained problems, many practical settings involve heterogeneous resource or model constraints,...
DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management
arXiv:2603.19621v1 Announce Type: new Abstract: Deep Reinforcement Learning (DRL) provides a general-purpose methodology for training inventory policies that can leverage big data and compute. However, off-the-shelf implementations of DRL have seen mixed success, often plagued by high sensitivity to the...
Continual Learning for Food Category Classification Dataset: Enhancing Model Adaptability and Performance
arXiv:2603.19624v1 Announce Type: new Abstract: Conventional machine learning pipelines often struggle to recognize categories absent from the original trainingset. This gap typically reduces accuracy, as fixed datasets rarely capture the full diversity of a domain. To address this, we propose...
Alternating Diffusion for Proximal Sampling with Zeroth Order Queries
arXiv:2603.19633v1 Announce Type: new Abstract: This work introduces a new approximate proximal sampler that operates solely with zeroth-order information of the potential function. Prior theoretical analyses have revealed that proximal sampling corresponds to alternating forward and backward iterations of the...
RiboSphere: Learning Unified and Efficient Representations of RNA Structures
arXiv:2603.19636v1 Announce Type: new Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce. We introduce \emph{RiboSphere}, a framework that learns \emph{discrete} geometric representations of...
Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
arXiv:2603.19648v1 Announce Type: new Abstract: Stochastic approximation (SA) is a fundamental iterative framework with broad applications in reinforcement learning and optimization. Classical analyses typically rely on martingale difference or Markov noise with bounded second moments, but many practical settings, including...
Scale-Dependent Radial Geometry and Metric Mismatch in Wasserstein Propagation for Reverse Diffusion
arXiv:2603.19670v1 Announce Type: new Abstract: Existing analyses of reverse diffusion often propagate sampling error in the Euclidean geometry underlying \(\Wtwo\) along the entire reverse trajectory. Under weak log-concavity, however, Gaussian smoothing can create contraction first at large separations while short...
An exclusive tour of Amazon’s Trainium lab, the chip that’s won over Anthropic, OpenAI, even Apple
Shortly after Amazon announced its $50 billion investment in OpenAI, AWS invited me on a private tour of the chip lab at the heart of the deal.
DOGE goes nuclear: How Trump invited Silicon Valley into America’s nuclear power regulator
“Assume the NRC is going to do whatever we tell the NRC to do.”
Why Wall Street wasn’t won over by Nvidia’s big conference
Despite investor fears of an AI bubble, Nvidia's latest conference shows that most in the industry aren't concerned by that possibility.
Unanimous court allows street preacher’s free speech case to move forward
A unanimous court on Friday sided with a Mississippi street preacher who sued to block future enforcement of a public demonstration ordinance that he was previously convicted of violating. A […]The postUnanimous court allows street preacher’s free speech case to...
Oral argument live blog for Wednesday, April 1
On Wednesday, April 1, we will be live blogging as the court hears argument in Trump v. Barbara, on the constitutionality of President Donald Trump’s executive order on birthright citizenship. […]The postOral argument live blog for Wednesday, April 1appeared first...
Justices to consider arbitration exemption for “last-mile” drivers
Flowers Foods v. Brock brings the justices another in a lengthening line of cases about the exemptions from the Federal Arbitration Act. The specific question is whether “last-mile” drivers – […]The postJustices to consider arbitration exemption for “last-mile” driversappeared first...
SCOTUStoday for Friday, March 20
The court has indicated that it may announce opinions this morning at 10 a.m. EDT. Our opinion day live blog begins at 9:30. Join us!The postSCOTUStoday for Friday, March 20appeared first onSCOTUSblog.
New court filing reveals Pentagon told Anthropic the two sides were nearly aligned — a week after Trump declared the relationship kaput
Anthropic submitted two sworn declarations to a California federal court late Friday afternoon, pushing back on the Pentagon's assertion that the AI company poses an "unacceptable risk to national security" and arguing that the government's case relies on technical misunderstandings...
Expert Personas Improve LLM Alignment but Damage Accuracy: Bootstrapping Intent-Based Persona Routing with PRISM
arXiv:2603.18507v1 Announce Type: new Abstract: Persona prompting can steer LLM generation towards a domain-specific tone and pattern. This behavior enables use cases in multi-agent systems where diverse interactions are crucial and human-centered tasks require high-level human alignment. Prior works provide...
EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research
arXiv:2603.18273v1 Announce Type: new Abstract: In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates end-to-end educational data mining (EDM) research. We conceptualize EDM-ARS as a general framework for domain-aware...
ZEBRAARENA: A Diagnostic Simulation Environment for Studying Reasoning-Action Coupling in Tool-Augmented LLMs
arXiv:2603.18614v1 Announce Type: new Abstract: Tool-augmented large language models (LLMs) must tightly couple multi-step reasoning with external actions, yet existing benchmarks often confound this interplay with complex environment dynamics, memorized knowledge or dataset contamination. In this paper, we introduce ZebraArena,...
Beyond Accuracy: An Explainability-Driven Analysis of Harmful Content Detection
arXiv:2603.18015v1 Announce Type: new Abstract: Although automated harmful content detection systems are frequently used to monitor online platforms, moderators and end users frequently cannot understand the logic underlying their predictions. While recent studies have focused on increasing classification accuracy, little...
Consumer-to-Clinical Language Shifts in Ambient AI Draft Notes and Clinician-Finalized Documentation: A Multi-level Analysis
arXiv:2603.18327v1 Announce Type: new Abstract: Ambient AI generates draft clinical notes from patient-clinician conversations, often using lay or consumer-oriented phrasing to support patient understanding instead of standardized clinical terminology. How clinicians revise these drafts for professional documentation conventions remains unclear....
A Computationally Efficient Learning of Artificial Intelligence System Reliability Considering Error Propagation
arXiv:2603.18201v1 Announce Type: new Abstract: Artificial Intelligence (AI) systems are increasingly prominent in emerging smart cities, yet their reliability remains a critical concern. These systems typically operate through a sequence of interconnected functional stages, where upstream errors may propagate to...
Do Large Language Models Possess a Theory of Mind? A Comparative Evaluation Using the Strange Stories Paradigm
arXiv:2603.18007v1 Announce Type: new Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to infer others' beliefs, intentions, and emotions from text. Given that LLMs are trained on language...
An Onto-Relational-Sophic Framework for Governing Synthetic Minds
arXiv:2603.18633v1 Announce Type: new Abstract: The rapid evolution of artificial intelligence, from task-specific systems to foundation models exhibiting broad, flexible competence across reasoning, creative synthesis, and social interaction, has outpaced the conceptual and governance frameworks designed to manage it. Current...
Reasonably reasoning AI agents can avoid game-theoretic failures in zero-shot, provably
arXiv:2603.18563v1 Announce Type: new Abstract: AI agents are increasingly deployed in interactive economic environments characterized by repeated AI-AI interactions. Despite AI agents' advanced capabilities, empirical studies reveal that such interactions often fail to stably induce a strategic equilibrium, such as...
Agentic Framework for Political Biography Extraction
arXiv:2603.18010v1 Announce Type: new Abstract: The production of large-scale political datasets typically demands extracting structured facts from vast piles of unstructured documents or web sources, a task that traditionally relies on expensive human experts and remains prohibitively difficult to automate...
NeuroGame Transformer: Gibbs-Inspired Attention Driven by Game Theory and Statistical Physics
arXiv:2603.18761v1 Announce Type: new Abstract: Standard attention mechanisms in transformers are limited by their pairwise formulation, which hinders the modeling of higher-order dependencies among tokens. We introduce the NeuroGame Transformer (NGT) to overcome this by reconceptualizing attention through a dual...
Analysis Of Linguistic Stereotypes in Single and Multi-Agent Generative AI Architectures
arXiv:2603.18729v1 Announce Type: new Abstract: Many works in the literature show that LLM outputs exhibit discriminatory behaviour, triggering stereotype-based inferences based on the dialect in which the inputs are written. This bias has been shown to be particularly pronounced when...