Thin Keys, Full Values: Reducing KV Cache via Low-Dimensional Attention Selection
arXiv:2603.04427v1 Announce Type: new Abstract: Standard transformer attention uses identical dimensionality for queries, keys, and values ($d_q = d_k = d_v = \dmodel$). Our insight is that these components serve fundamentally different roles, and this symmetry is unnecessary. Queries and...
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
arXiv:2603.04437v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption...
Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
arXiv:2603.04458v1 Announce Type: new Abstract: Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicate tendencies between two concepts (e.g., high and low temperature) with their...
MAD-SmaAt-GNet: A Multimodal Advection-Guided Neural Network for Precipitation Nowcasting
arXiv:2603.04461v1 Announce Type: new Abstract: Precipitation nowcasting (short-term forecasting) is still often performed using numerical solvers for physical equations, which are computationally expensive and make limited use of the large volumes of available weather data. Deep learning models have shown...
Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN
arXiv:2603.04477v1 Announce Type: new Abstract: Smart insoles equipped with pressure sensors, accelerometers, and gyroscopes offer a non-intrusive means of monitoring human gait and posture. We present an activity classification system based on a circular dilated convolutional neural network (CDCNN) that...
Standing on the Shoulders of Giants: Rethinking EEG Foundation Model Pretraining via Multi-Teacher Distillation
arXiv:2603.04478v1 Announce Type: new Abstract: Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely adapted from and inspired by the success of vision and language foundation models. However, unlike images and text, EEG...
Augmenting representations with scientific papers
arXiv:2603.04516v1 Announce Type: new Abstract: Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical sources. Still, these data sources are rarely systematically integrated. This work introduces a...
An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs
arXiv:2603.04545v1 Announce Type: new Abstract: Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference queries are computationally expensive and vary in complexity, as each involves a different number of...
A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments
arXiv:2603.04595v1 Announce Type: new Abstract: Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely heavily on direct identifiers such as names, emails,...
PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion
arXiv:2603.04606v1 Announce Type: new Abstract: PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific data. However, most downstream evaluations focus on forward problems, such as autoregressive...
When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
arXiv:2603.04648v1 Announce Type: new Abstract: Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and noise-free states. We study robustness of Proximal Policy Optimization (PPO) under temporally persistent...
Neuro-Symbolic Financial Reasoning via Deterministic Fact Ledgers and Adversarial Low-Latency Hallucination Detector
arXiv:2603.04663v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) architectures fail in high-stakes financial domains due to two fundamental limitations: the inherent arithmetic incompetence of Large Language Models (LLMs) and the distributional semantic conflation of dense vector retrieval (e.g., mapping...
Probabilistic Dreaming for World Models
arXiv:2603.04715v1 Announce Type: new Abstract: "Dreaming" enables agents to learn from imagined experiences, enabling more robust and sample-efficient learning of world models. In this work, we consider innovations to the state-of-the-art Dreamer model using probabilistic methods that enable: (1) the...
Distribution-Conditioned Transport
arXiv:2603.04736v1 Announce Type: new Abstract: Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific applications increasingly require models that can generalize to source and target distributions unseen...
ConTSG-Bench: A Unified Benchmark for Conditional Time Series Generation
arXiv:2603.04767v1 Announce Type: new Abstract: Conditional time series generation plays a critical role in addressing data scarcity and enabling causal analysis in real-world applications. Despite its increasing importance, the field lacks a standardized and systematic benchmarking framework for evaluating generative...
Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
arXiv:2603.04780v1 Announce Type: new Abstract: Causal discovery with latent variables is a fundamental task. Yet most existing methods rely on strong structural assumptions, such as enforcing specific indicator patterns for latents or restricting how they can interact with others. We...
Multilevel Training for Kolmogorov Arnold Networks
arXiv:2603.04827v1 Announce Type: new Abstract: Algorithmic speedup of training common neural architectures is made difficult by the lack of structure guaranteed by the function compositions inherent to such networks. In contrast to multilayer perceptrons (MLPs), Kolmogorov-Arnold networks (KANs) provide more...
Why Is RLHF Alignment Shallow? A Gradient Analysis
arXiv:2603.04851v1 Announce Type: new Abstract: Why is safety alignment in LLMs shallow? We prove that gradient-based alignment inherently concentrates on positions where harm is decided and vanishes beyond. Using a martingale decomposition of sequence-level harm, we derive an exact characterization...
Immigration Enforcement and Constraints on Information Commandeering
The debate over American immigration policy reflects deep moral divides over the meaning of American identity and the scope of fundamental individual rights like due process and the freedom of movement. Although the modern American immigration system no longer includes...
The Non-Punishment Principle and Restorative Justice
The non-punishment principle is a legal norm that has increasingly gained legitimacy over the past quarter-century within international, regional, and domestic law on human trafficking. At its core, this principle opposes the punishment of human trafficking victims for unlawful conduct...
Justices poised to adopt exceptions to federal criminal defendants’ appellate waivers
The Supreme Court heard oral argument on Tuesday in Hunter v. United States about what exceptions exist to federal defendants’ waivers of their right to appeal. The justices seemed poised […]The postJustices poised to adopt exceptions to federal criminal defendants’...
Birthright citizenship: the exceptions provide the rule
The battle over birthright citizenship is a battle over its exceptions. The 14th Amendment’s first sentence proudly proclaims that “[a]ll persons born . . . in the United States, and subject to the jurisdiction […]The postBirthright citizenship: the exceptions provide...
The emergency docket’s critics have it backwards
Ratio Decidendi is a recurring series by Stephanie Barclay exploring the reasoning – from practical considerations to deep theory – behind our nation’s most consequential constitutional decisions. Last Monday, the […]The postThe emergency docket’s critics have it backwardsappeared first onSCOTUSblog.
Anthropic’s Claude found 22 vulnerabilities in Firefox over two weeks
In a recent security partnership with Mozilla, Anthropic found 22 separate vulnerabilities in Firefox — 14 of them classified as "high-severity."
US reportedly considering sweeping new chip export controls
In an alleged drafted proposal, the U.S. government would play a role in every chip export sale regardless of which country it's coming from.
Luma launches creative AI agents powered by its new ‘Unified Intelligence’ models
Luma introduced Luma Agents, powered by its new “Unified Intelligence” models, designed to coordinate multiple AI systems and generate end-to-end creative work across text, images, video and audio.
OpenAI launches GPT-5.4 with Pro and Thinking versions
GPT-5.4 is billed as "our most capable and efficient frontier model for professional work."
AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents
arXiv:2603.03290v1 Announce Type: cross Abstract: Long-horizon LLM agents require memory systems that remain accurate under fixed context budgets. However, existing systems struggle with two persistent challenges in long-term dialogue: (i) \textbf{disconnected evidence}, where multi-hop answers require linking facts distributed across...
From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
arXiv:2603.03292v1 Announce Type: cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields. While Retrieval-Augmented Generation (RAG) mitigates these issues, existing methods...