Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
arXiv:2602.21498v1 Announce Type: new Abstract: Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative for learning temporal and variable dependencies. In addition, IMTS often exhibit diverse dependencies across...
Court rules criminal defendants may be prohibited from discussing ongoing testimony with counsel during an overnight recess
When a trial court recesses a criminal trial during a defendant’s testimony, the court may order the defendant and his lawyer not to discuss that testimony during the break except […]The postCourt rules criminal defendants may be prohibited from discussing...
Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
arXiv:2602.20306v1 Announce Type: new Abstract: High-fidelity computational models of cardiac mechanics provide mechanistic insight into the heart function but are computationally prohibitive for routine clinical use. Surrogate models can accelerate simulations, but generalization across diverse anatomies is challenging, particularly in...
Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning
arXiv:2602.18591v1 Announce Type: new Abstract: A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks is prohibitively expensive for large task sets, a...
From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
arXiv:2602.18793v1 Announce Type: new Abstract: Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GAD methods often focus on the learning paradigms of "one-model-for-one-dataset", requiring dataset-specific...
Oral argument live blog for Monday, March 2
On Monday, March 2, we will be live blogging as the court hears argument in United States v. Hemani, on whether a federal statute that prohibits gun possession by users […]The postOral argument live blog for Monday, March 2appeared first...
In Defense of Substantive Due Process
Introduction Originalism has a branding and substance problem.[1] If originalism is what it purports to be—impartial and value-free enforcement of the Founders’ intention and “the only approach to text that is compatible with democracy”[2]—more Americans would have faith in the...
Understanding Unreliability of Steering Vectors in Language Models: Geometric Predictors and the Limits of Linear Approximations
arXiv:2602.17881v1 Announce Type: cross Abstract: Steering vectors are a lightweight method for controlling language model behavior by adding a learned bias to the activations at inference time. Although effective on average, steering effect sizes vary across samples and are unreliable...
TFL: Targeted Bit-Flip Attack on Large Language Model
arXiv:2602.17837v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks. Recent studies have shown that bit-flip attacks (BFAs), which exploit computer...
X-MAP: eXplainable Misclassification Analysis and Profiling for Spam and Phishing Detection
arXiv:2602.15298v1 Announce Type: new Abstract: Misclassifications in spam and phishing detection are very harmful, as false negatives expose users to attacks while false positives degrade trust. Existing uncertainty-based detectors can flag potential errors, but possibly be deceived and offer limited...
Quantifying construct validity in large language model evaluations
arXiv:2602.15532v1 Announce Type: new Abstract: The LLM community often reports benchmark results as if they are synonymous with general model capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error. How can we know...
Combining scEEG and PPG for reliable sleep staging using lightweight wearables
arXiv:2602.15042v1 Announce Type: cross Abstract: Reliable sleep staging remains challenging for lightweight wearable devices such as single-channel electroencephalography (scEEG) or photoplethysmography (PPG). scEEG offers direct measurement of cortical activity and serves as the foundation for sleep staging, yet exhibits limited...
Genetic Generalized Additive Models
arXiv:2602.15877v1 Announce Type: cross Abstract: Generalized Additive Models (GAMs) balance predictive accuracy and interpretability, but manually configuring their structure is challenging. We propose using the multi-objective genetic algorithm NSGA-II to automatically optimize GAMs, jointly minimizing prediction error (RMSE) and a...
Decoding the Human Factor: High Fidelity Behavioral Prediction for Strategic Foresight
arXiv:2602.17222v1 Announce Type: new Abstract: Predicting human decision-making in high-stakes environments remains a central challenge for artificial intelligence. While large language models (LLMs) demonstrate strong general reasoning, they often struggle to generate consistent, individual-specific behavior, particularly when accurate prediction depends...
One-step Language Modeling via Continuous Denoising
arXiv:2602.16813v1 Announce Type: new Abstract: Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime,...
BankMathBench: A Benchmark for Numerical Reasoning in Banking Scenarios
arXiv:2602.17072v1 Announce Type: new Abstract: Large language models (LLMs)-based chatbots are increasingly being adopted in the financial domain, particularly in digital banking, to handle customer inquiries about products such as deposits, savings, and loans. However, these models still exhibit low...
Omitted Variable Bias in Language Models Under Distribution Shift
arXiv:2602.16784v1 Announce Type: cross Abstract: Despite their impressive performance on a wide variety of tasks, modern language models remain susceptible to distribution shifts, exhibiting brittle behavior when evaluated on data that differs in distribution from their training data. In this...
The creator economy’s ad revenue problem and India’s AI ambitions
The creator economy is evolving fast, and ad revenue alone isn’t cutting it anymore. YouTubers are launching product lines, acquiring startups, and building actual business empires. In fact, MrBeast’s company bought fintech startup Step, and his chocolate business is outearning...
Reranker Optimization via Geodesic Distances on k-NN Manifolds
arXiv:2602.15860v1 Announce Type: new Abstract: Current neural reranking approaches for retrieval-augmented generation (RAG) rely on cross-encoders or large language models (LLMs), requiring substantial computational resources and exhibiting latencies of 3-5 seconds per query. We propose Maniscope, a geometric reranking method...
What the Justice Department overlooks in its historical argument to end birthright citizenship
Immigration Matters is a recurring series by César Cuauhtémoc García Hernández that analyzes the court’s immigration docket, highlighting emerging legal questions about new policy and enforcement practices. In my last […]The postWhat the Justice Department overlooks in its historical argument...
Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
arXiv:2602.15198v1 Announce Type: cross Abstract: Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks. This surfaces a unique safety problem when individual agents form a coalition and \emph{collude} to pursue secondary goals...
Scenario-Adaptive MU-MIMO OFDM Semantic Communication With Asymmetric Neural Network
arXiv:2602.13557v1 Announce Type: new Abstract: Semantic Communication (SemCom) has emerged as a promising paradigm for 6G networks, aiming to extract and transmit task-relevant information rather than minimizing bit errors. However, applying SemCom to realistic downlink Multi-User Multi-Input Multi-Output (MU-MIMO) Orthogonal...
AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning
arXiv:2602.13807v1 Announce Type: new Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings. However, most existing methods frame anomaly detection as a purely discriminative...