Justices will hear argument on Trump administration’s removal of protected status for Syrian and Haitian nationals
The Supreme Court announced on Monday afternoon that it will hear oral argument on whether the Trump administration can end a program that allows several thousand Syrians and approximately 350,000 […]The postJustices will hear argument on Trump administration’s removal of...
Haitian nationals ask court to deny Trump administration’s request to remove their protected status
A group of Haitian nationals urged the Supreme Court on Monday to leave in place a ruling by a federal judge in Washington, D.C., that allows them to stay in […]The postHaitian nationals ask court to deny Trump administration’s request...
Birthright citizenship: a response to Pete Patterson
Brothers in Law is a recurring series by brothers Akhil and Vikram Amar, with special emphasis on measuring what the Supreme Court says against what the Constitution itself says. For more content from […]The postBirthright citizenship: a response to Pete...
A 95th birthday tribute to legendary SCOTUSblog reporter Lyle Denniston
The inimitable Lyle Denniston, who served as the primary reporter for SCOTUSblog from 2004 until 2016, celebrates his 95th birthday today. Lyle began his reporting career in 1948 at the […]The postA 95th birthday tribute to legendary SCOTUSblog reporter Lyle...
SCOTUStoday: Trump v. the Fed
Six years ago today, the court announced that it was postponing its March argument session in response to the COVID-19 pandemic. The press release noted that its “postponement of argument […]The postSCOTUStoday: Trump v. the Fedappeared first onSCOTUSblog.
HCP-DCNet: A Hierarchical Causal Primitive Dynamic Composition Network for Self-Improving Causal Understanding
arXiv:2603.12305v1 Announce Type: cross Abstract: The ability to understand and reason about cause and effect -- encompassing interventions, counterfactuals, and underlying mechanisms -- is a cornerstone of robust artificial intelligence. While deep learning excels at pattern recognition, it fundamentally lacks...
Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
arXiv:2603.12933v1 Announce Type: new Abstract: Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the quality--cost trade-off space. Despite these advances, real-world deployment is often constrained...
Prompt Injection as Role Confusion
arXiv:2603.12277v1 Announce Type: cross Abstract: Language models remain vulnerable to prompt injection attacks despite extensive safety training. We trace this failure to role confusion: models infer roles from how text is written, not where it comes from. We design novel...
Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations
arXiv:2603.12813v1 Announce Type: new Abstract: Agentic AI systems integrating large language models (LLMs) with reasoning and tooluse capabilities are transforming various domains - in particular, software development. In contrast, their application in chemical process flowsheet modelling remains largely unexplored. In...
Operationalising Cyber Risk Management Using AI: Connecting Cyber Incidents to MITRE ATT&CK Techniques, Security Controls, and Metrics
arXiv:2603.12455v1 Announce Type: cross Abstract: The escalating frequency of cyber-attacks poses significant challenges for organisations, particularly small enterprises constrained by limited in-house expertise, insufficient knowledge, and financial resources. This research presents a novel framework that leverages Natural Language Processing to...
CSE-UOI at SemEval-2026 Task 6: A Two-Stage Heterogeneous Ensemble with Deliberative Complexity Gating for Political Evasion Detection
arXiv:2603.12453v1 Announce Type: new Abstract: This paper describes our system for SemEval-2026 Task 6, which classifies clarity of responses in political interviews into three categories: Clear Reply, Ambivalent, and Clear Non-Reply. We propose a heterogeneous dual large language model (LLM)...
RTD-Guard: A Black-Box Textual Adversarial Detection Framework via Replacement Token Detection
arXiv:2603.12582v1 Announce Type: new Abstract: Textual adversarial attacks pose a serious security threat to Natural Language Processing (NLP) systems by introducing imperceptible perturbations that mislead deep learning models. While adversarial example detection offers a lightweight alternative to robust training, existing...
MetaKE: Meta-learning Aligned Knowledge Editing via Bi-level Optimization
arXiv:2603.12677v1 Announce Type: new Abstract: Knowledge editing (KE) aims to precisely rectify specific knowledge in Large Language Models (LLMs) without disrupting general capabilities. State-of-the-art methods suffer from an open-loop control mismatch. We identify a critical "Semantic-Execution Disconnect": the semantic target...
NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation
arXiv:2603.12378v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) techniques, particularly Low-Rank Adaptation (LoRA), have become essential for adapting Large Language Models (LLMs) to downstream tasks. While the recent FlyLoRA framework successfully leverages bio-inspired sparse random projections to mitigate parameter interference,...
No More DeLuLu: Physics-Inspired Kernel Networks for Geometrically-Grounded Neural Computation
arXiv:2603.12276v1 Announce Type: new Abstract: We introduce the yat-product, a kernel operator combining quadratic alignment with inverse-square proximity. We prove it is a Mercer kernel, analytic, Lipschitz on bounded domains, and self-regularizing, admitting a unique RKHS embedding. Neural Matter Networks...
Multi-objective Genetic Programming with Multi-view Multi-level Feature for Enhanced Protein Secondary Structure Prediction
arXiv:2603.12293v1 Announce Type: new Abstract: Predicting protein secondary structure is essential for understanding protein function and advancing drug discovery. However, the intricate sequence-structure relationship poses significant challenges for accurate modeling. To address these, we propose MOGP-MMF, a multi-objective genetic programming...
Adaptive Conditional Forest Sampling for Spectral Risk Optimisation under Decision-Dependent Uncertainty
arXiv:2603.12507v1 Announce Type: new Abstract: Minimising a spectral risk objective, defined as a convex combination of expected cost and Conditional Value-at-Risk (CVaR), is challenging when the uncertainty distribution is decision-dependent, making both surrogate modelling and simulation-based ranking sensitive to tail...
Learning Pore-scale Multiphase Flow from 4D Velocimetry
arXiv:2603.12516v1 Announce Type: new Abstract: Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce...
Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors
arXiv:2603.12540v1 Announce Type: new Abstract: Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQML is technically feasible only in limited...
Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity
arXiv:2603.12556v1 Announce Type: new Abstract: We establish empirical scaling laws for Single-Layer Physics-Informed Neural Networks on canonical nonlinear PDEs. We identify a dual optimization failure: (i) a baseline pathology, where the solution error fails to decrease with network width, even...
Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
arXiv:2603.12676v1 Announce Type: new Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harder and optimization less stable. The problem becomes even more severe when the model must also predict...
Announcement of opinions for Friday, March 20
On Friday, March 20, we will be live blogging as the court potentially releases opinions in one or more argued cases from the current term. Click here for a list […]The postAnnouncement of opinions for Friday, March 20appeared first onSCOTUSblog.
Does legislative history have a judicial future?
Major Questions is a recurring series by Adam White, which analyzes the court’s approach to administrative law, agencies, and the lower courts. Does legislative history have a future in judicial […]The postDoes legislative history have a judicial future?appeared first onSCOTUSblog.
Is Justice Alito jumping the gun on voting rights?
Cases and Controversies is a recurring series by Carolyn Shapiro, primarily focusing on the effects of the Supreme Court’s rulings, opinions, and procedures on the law, on other institutions, and on […]The postIs Justice Alito jumping the gun on voting...
SCOTUStoday for Friday, March 13
President Chester A. Arthur nominated Justice Samuel Blatchford to the court on this day in 1882. According to Justia, Blatchford was a “precocious talent” who “enrolled in Columbia College (now […]The postSCOTUStoday for Friday, March 13appeared first onSCOTUSblog.
Training Is Everything: Artificial Intelligence, Copyright, and Fair Training
To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many of which fall within the core subject matter of copyright law. To some, the use...
A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms
arXiv:2603.11093v1 Announce Type: new Abstract: The development of high-level autonomous driving (AD) is shifting from perception-centric limitations to a more fundamental bottleneck, namely, a deficit in robust and generalizable reasoning. Although current AD systems manage structured environments, they consistently falter...
Measuring AI Agents' Progress on Multi-Step Cyber Attack Scenarios
arXiv:2603.11214v1 Announce Type: new Abstract: We evaluate the autonomous cyber-attack capabilities of frontier AI models on two purpose-built cyber ranges-a 32-step corporate network attack and a 7-step industrial control system attack-that require chaining heterogeneous capabilities across extended action sequences. By...
LLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction
arXiv:2603.11446v1 Announce Type: new Abstract: Mainstream methods for Legal Judgment Prediction (LJP) based on Pre-trained Language Models (PLMs) heavily rely on the statistical correlation between case facts and judgment results. This paradigm lacks explicit modeling of legal constituent elements and...