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...
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...
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...
VisDoT : Enhancing Visual Reasoning through Human-Like Interpretation Grounding and Decomposition of Thought
arXiv:2603.11631v1 Announce Type: new Abstract: Large vision-language models (LVLMs) struggle to reliably detect visual primitives in charts and align them with semantic representations, which severely limits their performance on complex visual reasoning. This lack of perceptual grounding constitutes a major...
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...
AI Knows What's Wrong But Cannot Fix It: Helicoid Dynamics in Frontier LLMs Under High-Stakes Decisions
arXiv:2603.11559v1 Announce Type: new Abstract: Large language models perform reliably when their outputs can be checked: solving equations, writing code, retrieving facts. They perform differently when checking is impossible, as when a clinician chooses an irreversible treatment on incomplete data,...
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...
An interview with Jerry Goldman, founder of the Oyez Project
Welcome to our SCOTUS Innovators series, a new recurring column on people who have shaped our understanding of the Supreme Court. A few weeks ago, I had the opportunity to […]The postAn interview with Jerry Goldman, founder of the Oyez...
Trump's DOJ is not falling for Sam Bankman-Fried's MAGA makeover on X
SBF is still twisting facts to hide FTX crypto losses, DOJ says to block new trial.
DeliberationBench: A Normative Benchmark for the Influence of Large Language Models on Users' Views
arXiv:2603.10018v1 Announce Type: cross Abstract: As large language models (LLMs) become pervasive as assistants and thought partners, it is important to characterize their persuasive influence on users' beliefs. However, a central challenge is to distinguish "beneficial" from "harmful" forms of...
Quantifying Hallucinations in Language Language Models on Medical Textbooks
arXiv:2603.09986v1 Announce Type: cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to...
A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance
arXiv:2603.09999v1 Announce Type: cross Abstract: This paper presents the design and validation of a retrieval-based assistant that supports safety assessment, certification activities, and regulatory compliance for unmanned aircraft systems. The work is motivated by the growing complexity of drone operations...
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
arXiv:2603.10396v1 Announce Type: new Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elicitation techniques developed under the classical probabilistic uncertainty framework. This...
S-GRADES -- Studying Generalization of Student Response Assessments in Diverse Evaluative Settings
arXiv:2603.10233v1 Announce Type: new Abstract: Evaluating student responses, from long essays to short factual answers, is a key challenge in educational NLP. Automated Essay Scoring (AES) focuses on holistic writing qualities such as coherence and argumentation, while Automatic Short Answer...
Revisiting Sharpness-Aware Minimization: A More Faithful and Effective Implementation
arXiv:2603.10048v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) enhances generalization by minimizing the maximum training loss within a predefined neighborhood around the parameters. However, its practical implementation approximates this as gradient ascent(s) followed by applying the gradient at the ascent...
Discovery of a Hematopoietic Manifold in scGPT Yields a Method for Extracting Performant Algorithms from Biological Foundation Model Internals
arXiv:2603.10261v1 Announce Type: new Abstract: We report the discovery and extraction of a compact hematopoietic algorithm from the single-cell foundation model scGPT, to our knowledge the first biologically useful, competitive algorithm extracted from a foundation model via mechanistic interpretability. We...
Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
arXiv:2603.10281v1 Announce Type: new Abstract: While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization algorithms such as ADMM remains nontrivial. Two central challenges arise: i) the mismatch between the noisy data...
Trump administration urges Supreme Court to allow it to revoke protected status for Haitian nationals
The Trump administration on Wednesday asked the Supreme Court to pause a ruling by a federal judge in Washington, D.C., that barred the government from ending a program that allows […]The postTrump administration urges Supreme Court to allow it to...
SCOTUStoday for Wednesday, March 11
You’ve likely heard of AI bots being used improperly by lawyers, but what about lawsuits over AI bots practicing law without a license? Reuters reported on one such case last […]The postSCOTUStoday for Wednesday, March 11appeared first onSCOTUSblog.
Making the Rules of the Rules of the Game: The Use, Misuse, and Disuse of the Rulemaking Grant in the Act of 1842
Introduction On August 23, 1842, Congress quietly and quickly conferred a broad grant of rulemaking authority on the Supreme Court. The Act of Aug....The postMaking the Rules of the Rules of the Game: The Use, Misuse, and Disuse of the...
Common Sense vs. Morality: The Curious Case of Narrative Focus Bias in LLMs
arXiv:2603.09434v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse real-world applications and user communities. As such, it is crucial that these models remain both morally grounded and knowledge-aware. In this work, we uncover a critical...
LDP: An Identity-Aware Protocol for Multi-Agent LLM Systems
arXiv:2603.08852v1 Announce Type: new Abstract: As multi-agent AI systems grow in complexity, the protocols connecting them constrain their capabilities. Current protocols such as A2A and MCP do not expose model-level properties as first-class primitives, ignoring properties fundamental to effective delegation:...
Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG
arXiv:2603.09758v1 Announce Type: new Abstract: Standardizing food terms from product labels and menus into ontology concepts is a prerequisite for trustworthy dietary assessment and safety reporting. The dominant approach to Named Entity Linking (NEL) in the food and nutrition domains...
Multi-level meta-reinforcement learning with skill-based curriculum
arXiv:2603.08773v1 Announce Type: new Abstract: We consider problems in sequential decision making with natural multi-level structure, where sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure has remained a longstanding challenge; we describe an efficient...
Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation
arXiv:2603.09053v1 Announce Type: new Abstract: Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment, and has become essential in mission-critical domains such as supply chains and industrial systems. However, simulators learned from noisy or biased real-world...
Proxy-Guided Measurement Calibration
arXiv:2603.09288v1 Announce Type: new Abstract: Aggregate outcome variables collected through surveys and administrative records are often subject to systematic measurement error. For instance, in disaster loss databases, county-level losses reported may differ from the true damages due to variations in...
The how and why of gun control
A Second Opinion is a recurring series by Haley Proctor on the Second Amendment and constitutional litigation. Last Monday, the Supreme Court heard argument in United States v. Hemani. In […]The postThe how and why of gun controlappeared first onSCOTUSblog.
AI Now Co-ED Amba Kak Gives Remarks Before the UN General Assembly on AI Governance - AI Now Institute
Dissecting racial bias in an algorithm used to manage the health of populations
Racial bias in health algorithms The U.S. health care system uses commercial algorithms to guide health decisions. Obermeyer et al. find evidence of racial bias in one widely used algorithm, such that Black patients assigned the same level of risk...
A Dynamic Self-Evolving Extraction System
arXiv:2603.06915v1 Announce Type: new Abstract: The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation. High-quality extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and...