Provably Safe Generative Sampling with Constricting Barrier Functions
arXiv:2602.21429v1 Announce Type: new Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions. However, a critical gap remains for their deployment in safety-critical domains: the lack of formal...
When Learning Hurts: Fixed-Pole RNN for Real-Time Online Training
arXiv:2602.21454v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) can be interpreted as discrete-time state-space models, where the state evolution corresponds to an infinite-impulse-response (IIR) filtering operation governed by both feedforward weights and recurrent poles. While, in principle, all parameters...
Geometric Priors for Generalizable World Models via Vector Symbolic Architecture
arXiv:2602.21467v1 Announce Type: new Abstract: A key challenge in artificial intelligence and neuroscience is understanding how neural systems learn representations that capture the underlying dynamics of the world. Most world models represent the transition function with unstructured neural networks, limiting...
D-Flow SGLD: Source-Space Posterior Sampling for Scientific Inverse Problems with Flow Matching
arXiv:2602.21469v1 Announce Type: new Abstract: Data assimilation and scientific inverse problems require reconstructing high-dimensional physical states from sparse and noisy observations, ideally with uncertainty-aware posterior samples that remain faithful to learned priors and governing physics. While training-free conditional generation is...
GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning
arXiv:2602.21492v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problems. This sensitivity stems from the non-stationarity of RL: rollouts...
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...
WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck
arXiv:2602.21508v1 Announce Type: new Abstract: Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We identify that existing methods fail because they entangle the watermark with high-frequency cover texture, which...
Training Generalizable Collaborative Agents via Strategic Risk Aversion
arXiv:2602.21515v1 Announce Type: new Abstract: Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals. Unfortunately, existing approaches to learning policies for such collaborative problems produce brittle solutions that fail when paired with...
Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
arXiv:2602.21546v1 Announce Type: new Abstract: The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assigning jobs to various machines to optimize criteria, such as minimizing total completion...
Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction
arXiv:2602.21550v1 Announce Type: new Abstract: Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of...
From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
arXiv:2602.21551v1 Announce Type: new Abstract: Learning PDE dynamics for fluids increasingly relies on neural operators and Transformer-based models, yet these approaches often lack interpretability and struggle with localized, high-frequency structures while incurring quadratic cost in spatial samples. We propose representing...
Training-free Composition of Pre-trained GFlowNets for Multi-Objective Generation
arXiv:2602.21565v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial. Further extending GFlowNets to multi-objective settings has...
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
arXiv:2602.21585v1 Announce Type: new Abstract: Many applications seek to optimize LLM outputs at test time by iteratively proposing, scoring, and refining candidates over a discrete output space. Existing methods use a calibrated scalar evaluator for the target objective to guide...
ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
arXiv:2602.21588v1 Announce Type: new Abstract: Agent-based epidemic models (ABMs) encode behavioral and policy heterogeneity but are too slow for nightly hospital planning. We develop county-ready surrogates that learn directly from exascale ABM trajectories using Universal Differential Equations (UDEs): mechanistic SEIR-family...
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence
Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. In recent years, LegalAI has drawn increasing attention rapidly from both AI researchers and legal professionals, as...
Copyright’s Invisible Hand: Subsidizing America’s Cultural Institutions
The doctrine of copyright exhaustion conceals a substantial and underappreciated subsidy at the heart of American copyright law. For more than a century, it has operated as a deliberate congressional scheme transferring billions of dollars in value to cultural institutions,...
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...
AI’s Future May Be Quantum
Stephanie Seoyun Hwang, J.D. Class of 2028 While most people recognize AI as a transformative force, fewer are aware of one of the key technologies fueling its progress: quantum computing. In fact, many governments and tech industry actors see it...
Third Time’s the Charm? The Fate of the EU–U.S. Data Privacy Framework
Ksheeraja Satish, LL.M. Class of 2026 Transatlantic transfers of personal data are fundamental to the global digital economy. However, the legal history of these transfer mechanisms has been one of successive invalidations. Each time the European Union (EU) and the...
Anthropic CEO stands firm as Pentagon deadline looms
Anthropic CEO Dario Amodei said Thursday that he "cannot in good conscience accede" to the Pentagon's demands to give the military unrestricted access to its AI systems.
Enhancing Hate Speech Detection on Social Media: A Comparative Analysis of Machine Learning Models and Text Transformation Approaches
arXiv:2602.20634v1 Announce Type: new Abstract: The proliferation of hate speech on social media platforms has necessitated the development of effective detection and moderation tools. This study evaluates the efficacy of various machine learning models in identifying hate speech and offensive...
Semantic Novelty at Scale: Narrative Shape Taxonomy and Readership Prediction in 28,606 Books
arXiv:2602.20647v1 Announce Type: new Abstract: I introduce semantic novelty--cosine distance between each paragraph's sentence embedding and the running centroid of all preceding paragraphs--as an information-theoretic measure of narrative structure at corpus scale. Applying it to 28,606 books in PG19 (pre-1920...
CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models
arXiv:2602.20648v1 Announce Type: new Abstract: Client perceptions of the therapeutic alliance are critical for counseling effectiveness. Accurately capturing these perceptions remains challenging, as traditional post-session questionnaires are burdensome and often delayed, while existing computational approaches produce coarse scores, lack interpretable...
CAMEL: Confidence-Gated Reflection for Reward Modeling
arXiv:2602.20670v1 Announce Type: new Abstract: Reward models play a fundamental role in aligning large language models with human preferences. Existing methods predominantly follow two paradigms: scalar discriminative preference models, which are efficient but lack interpretability, and generative judging models, which...
Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization
arXiv:2602.20743v1 Announce Type: new Abstract: Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation varies with the data domain, privacy objectives, and downstream application. However, existing anonymization methods rely on static, manually...