Court rejects ICE contractor’s right to immediate appeal
The opinion yesterday in The GEO Group v. Menocal rejects the efforts of a contractor for ICE to get an immediate appeal from a district court judgment. The case involves […]The postCourt rejects ICE contractor’s right to immediate appealappeared first...
Beach blasts and unusually dangerous weapons
The Relist Watch column examines cert petitions that the Supreme Court has “relisted” for its upcoming conference. A short explanation of relists is available here. With the rest of the current […]The postBeach blasts and unusually dangerous weaponsappeared first onSCOTUSblog.
Trump administration asks justices to allow it to remove protected status from Syrian nationals
The Trump administration on Thursday asked the Supreme Court to freeze a ruling by a federal judge in New York that indefinitely postpones the termination of a program that allows […]The postTrump administration asks justices to allow it to remove...
Court to hear argument on whether and when drug users may possess firearms
The Supreme Court will hear oral arguments on Monday in United States v. Hemani, the second gun-rights case of the 2025-26 term. In January, the Trump administration supported Hawaii gun […]The postCourt to hear argument on whether and when drug...
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...
How can the Supreme Court protect electoral integrity?
Justice, Democracy, and Law is a recurring series by Edward B. Foley that focuses on election law and the relationship of law and democracy. The court has already confronted cases […]The postHow can the Supreme Court protect electoral integrity?appeared first...
Overton Pluralistic Reinforcement Learning for Large Language Models
arXiv:2602.20759v1 Announce Type: new Abstract: Existing alignment paradigms remain limited in capturing the pluralistic nature of human values. Overton Pluralism addresses this gap by generating responses with diverse perspectives from a single query. This paper introduces OP-GRPO (Overton Pluralistic Group...
Beyond the Star Rating: A Scalable Framework for Aspect-Based Sentiment Analysis Using LLMs and Text Classification
arXiv:2602.21082v1 Announce Type: new Abstract: Customer-provided reviews have become an important source of information for business owners and other customers alike. However, effectively analyzing millions of unstructured reviews remains challenging. While large language models (LLMs) show promise for natural language...
PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data
arXiv:2602.21165v1 Announce Type: new Abstract: Patient-generated text such as secure messages, surveys, and interviews contains rich expressions of the patient voice (PV), reflecting communicative behaviors and social determinants of health (SDoH). Traditional qualitative coding frameworks are labor intensive and do...
Tensor Network Generator-Enhanced Optimization for Traveling Salesman Problem
arXiv:2602.20175v1 Announce Type: new Abstract: We present an application of the tensor network generator-enhanced optimization (TN-GEO) framework to address the traveling salesman problem (TSP), a fundamental combinatorial optimization challenge. Our approach employs a tensor network Born machine based on automatically...
IMOVNO+: A Regional Partitioning and Meta-Heuristic Ensemble Framework for Imbalanced Multi-Class Learning
arXiv:2602.20199v1 Announce Type: new Abstract: Class imbalance, overlap, and noise degrade data quality, reduce model reliability, and limit generalization. Although widely studied in binary classification, these issues remain underexplored in multi-class settings, where complex inter-class relationships make minority-majority structures unclear...
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...
Emergent Manifold Separability during Reasoning in Large Language Models
arXiv:2602.20338v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting significantly improves reasoning in Large Language Models, yet the temporal dynamics of the underlying representation geometry remain poorly understood. We investigate these dynamics by applying Manifold Capacity Theory (MCT) to a compositional...
Wasserstein Distributionally Robust Online Learning
arXiv:2602.20403v1 Announce Type: new Abstract: We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from a Wasserstein ambiguity set centered at past observations. While this paradigm is well understood in...
GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization
arXiv:2602.20427v1 Announce Type: new Abstract: Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to replace traditional ones like exact solvers or heuristics with gradient-based search, they typically rely on...
CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detection
arXiv:2602.20468v1 Announce Type: new Abstract: Multivariate time-series anomaly detection is essential for reliable industrial control, telemetry, and service monitoring. However, the evolving inter-variable dependencies and inevitable noise render it challenging. Existing methods often use single-scale graphs or instance-level contrast. Moreover,...
GENSR: Symbolic Regression Based in Equation Generative Space
arXiv:2602.20557v1 Announce Type: new Abstract: Symbolic Regression (SR) tries to reveal the hidden equations behind observed data. However, most methods search within a discrete equation space, where the structural modifications of equations rarely align with their numerical behavior, leaving fitting...
Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets
arXiv:2602.20578v1 Announce Type: new Abstract: We study online maximization of non-monotone Diminishing-Return(DR)-submodular functions over down-closed convex sets, a regime where existing projection-free online methods suffer from suboptimal regret and limited feedback guarantees. Our main contribution is a new structural result...
Justices send litigation about tainted baby food back to state court
Yesterday’s decision in The Hain Celestial Group v Palmquist resolves a technical problem about what to do when district courts make a mistaken ruling about their own jurisdiction. The final […]The postJustices send litigation about tainted baby food back to...
Justices reveal little about whether the deadline for removing cases to federal court can be excused
When a plaintiff files a lawsuit in state court asserting a claim that could be brought in federal court, federal law gives the defendant 30 days to remove the case […]The postJustices reveal little about whether the deadline for removing...
SCOTUStoday for Wednesday, February 25: SCOTUS and the State of the Union
Another day, another live blog. Join us to discuss the possible announcement of opinions this morning beginning at 9:30 a.m. EST.The postSCOTUStoday for Wednesday, February 25: SCOTUS and the State of the Unionappeared first onSCOTUSblog.
Judge doesn't trust DOJ with search of devices seized from Wash. Post reporter
Court to search devices itself instead of letting government have full access.
Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content
arXiv:2602.19177v1 Announce Type: new Abstract: The increasing use of Large Language Models (LLMs) as proxies for human participants in social science research presents a promising, yet methodologically risky, paradigm shift. While LLMs offer scalability and cost-efficiency, their "naive" application, where...
DEEP: Docker-based Execution and Evaluation Platform
arXiv:2602.19583v1 Announce Type: new Abstract: Comparative evaluation of several systems is a recurrent task in researching. It is a key step before deciding which system to use for our work, or, once our research has been conducted, to demonstrate the...
Revisiting the Seasonal Trend Decomposition for Enhanced Time Series Forecasting
arXiv:2602.18465v1 Announce Type: new Abstract: Time series forecasting presents significant challenges in real-world applications across various domains. Building upon the decomposition of the time series, we enhance the architecture of machine learning models for better multivariate time series forecasting. To...
Support Vector Data Description for Radar Target Detection
arXiv:2602.18486v1 Announce Type: new Abstract: Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is better modeled...
Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment
arXiv:2602.18572v1 Announce Type: new Abstract: Reliable real estate price indicators are typically published at city level and low frequency, limiting their use for neighborhood-scale monitoring and long-horizon planning. We study whether sub-city price indices can be forecasted at weekly frequency...
Learning Beyond Optimization: Stress-Gated Dynamical Regime Regulation in Autonomous Systems
arXiv:2602.18581v1 Announce Type: new Abstract: Despite their apparent diversity, modern machine learning methods can be reduced to a remarkably simple core principle: learning is achieved by continuously optimizing parameters to minimize or maximize a scalar objective function. This paradigm has...
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...
Learning Invariant Visual Representations for Planning with Joint-Embedding Predictive World Models
arXiv:2602.18639v1 Announce Type: new Abstract: World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-level reconstruction. However, recent latent predictive architectures (JEPAs), including the DINO world model (DINO-WM), display a...