A Dynamic Bayesian and Machine Learning Framework for Quantitative Evaluation and Prediction of Operator Situation Awareness in Nuclear Power Plants
arXiv:2603.19298v1 Announce Type: new Abstract: Operator situation awareness is a pivotal yet elusive determinant of human reliability in complex nuclear control environments. Existing assessment methods, such as SAGAT and SART, remain static, retrospective, and detached from the evolving cognitive dynamics...
GT-Space: Enhancing Heterogeneous Collaborative Perception with Ground Truth Feature Space
arXiv:2603.19308v1 Announce Type: new Abstract: In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key challenge lies in handling {\em heterogeneous} features from agents equipped with different sensing modalities or model architectures,...
LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
arXiv:2603.19312v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision...
MSNet and LS-Net: Scalable Multi-Scale Multi-Representation Networks for Time Series Classification
arXiv:2603.19315v1 Announce Type: new Abstract: Time series classification (TSC) performance depends not only on architectural design but also on the diversity of input representations. In this work, we propose a scalable multi-scale convolutional framework that systematically integrates structured multi-representation inputs...
FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions
arXiv:2603.19331v1 Announce Type: new Abstract: Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational inference can efficiently estimate the joint posterior distribution of boundary conditions, with...
Beyond Weighted Summation: Learnable Nonlinear Aggregation Functions for Robust Artificial Neurons
arXiv:2603.19344v1 Announce Type: new Abstract: Weighted summation has remained the default input aggregation mechanism in artificial neurons since the earliest neural network models. While computationally efficient, this design implicitly behaves like a mean-based estimator and is therefore sensitive to noisy...
Anatomical Heterogeneity in Transformer Language Models
arXiv:2603.19348v1 Announce Type: new Abstract: Current transformer language models are trained with uniform computational budgets across all layers, implicitly assuming layer homogeneity. We challenge this assumption through empirical analysis of SmolLM2-135M, a 30-layer, 135M-parameter causal language model, using five diagnostic...
Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning
arXiv:2603.19397v1 Announce Type: new Abstract: Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are often constrained by limited resources, particularly in early outbreak stages. In real-world public health settings, resources must...
Global Convergence of Multiplicative Updates for the Matrix Mechanism: A Collaborative Proof with Gemini 3
arXiv:2603.19465v1 Announce Type: new Abstract: We analyze a fixed-point iteration $v \leftarrow \phi(v)$ arising in the optimization of a regularized nuclear norm objective involving the Hadamard product structure, posed in~\cite{denisov} in the context of an optimization problem over the space...
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
arXiv:2603.19497v1 Announce Type: new Abstract: Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to anomaly-free training samples, fully unsupervised settings with unlabeled and potentially contaminated training data, and semi-supervised settings...
Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering
arXiv:2603.19501v1 Announce Type: new Abstract: Graph filters leverage topological information to process networked data with existing methods mainly studying fixed graphs, ignoring that graphs often expand as nodes continually attach with an unknown pattern. The latter requires developing filter-based decision-making...
Subspace Kernel Learning on Tensor Sequences
arXiv:2603.19546v1 Announce Type: new Abstract: Learning from structured multi-way data, represented as higher-order tensors, requires capturing complex interactions across tensor modes while remaining computationally efficient. We introduce Uncertainty-driven Kernel Tensor Learning (UKTL), a novel kernel framework for $M$-mode tensors that...
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...
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...
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...
The Residual Stream Is All You Need: On the Redundancy of the KV Cache in Transformer Inference
arXiv:2603.19664v1 Announce Type: new Abstract: The key-value (KV) cache is widely treated as essential state in transformer inference, and a large body of work engineers policies to compress, evict, or approximate its entries. We prove that this state is entirely...
GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
arXiv:2603.19677v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the underlying communication topology that coordinates agent interactions. Within these systems, successful problem-solving often...
Elon Musk unveils chip manufacturing plans for SpaceX and Tesla
Elon Musk recently outlined ambitious plans for a chip-building collaboration Tesla and SpaceX — but he has a history of overpromising.
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.
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...
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...
What happened at Nvidia GTC: NemoClaw, Robot Olaf, and a $1 trillion bet
CEO Jensen Huang took the stage at Nvidia’s GTC conference this week in his signature leather jacket to deliver a two-and-a-half-hour keynote, projecting $1 trillion in AI chip sales through 2027, declaring that every company needs an “OpenClaw strategy,” and...
Nvidia has an OpenClaw strategy. Do you?
CEO Jensen Huang took the stage at Nvidia’s GTC conference this week in his signature leather jacket to deliver a two-and-a-half-hour keynote, projecting $1 trillion in AI chip sales through 2027, declaring that every company needs an “OpenClaw strategy,” and...
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...
DEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models
arXiv:2603.18048v1 Announce Type: new Abstract: Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuinely process acoustic signals or rely on text-based semantic inference. To systematically study this...
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...