Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems
arXiv:2603.15927v1 Announce Type: new Abstract: We propose a data-driven framework to learn interaction kernels in stochastic multi-agent systems. Our approach aims at identifying the functional form of nonlocal interaction and diffusion terms directly from trajectory data, without any a priori...
Residual Stream Duality in Modern Transformer Architectures
arXiv:2603.16039v1 Announce Type: new Abstract: Recent work has made clear that the residual pathway is not mere optimization plumbing; it is part of the model's representational machinery. We agree, but argue that the cleanest way to organize this design space...
Collaborative Temporal Feature Generation via Critic-Free Reinforcement Learning for Cross-User Sensor-Based Activity Recognition
arXiv:2603.16043v1 Announce Type: new Abstract: Human Activity Recognition using wearable inertial sensors is foundational to healthcare monitoring, fitness analytics, and context-aware computing, yet its deployment is hindered by cross-user variability arising from heterogeneous physiological traits, motor habits, and sensor placements....
Adaptive regularization parameter selection for high-dimensional inverse problems: A Bayesian approach with Tucker low-rank constraints
arXiv:2603.16066v1 Announce Type: new Abstract: This paper introduces a novel variational Bayesian method that integrates Tucker decomposition for efficient high-dimensional inverse problem solving. The method reduces computational complexity by transforming variational inference from a high-dimensional space to a lower-dimensional core...
MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Compute-optimal Scaling of Diffusion Language Models
arXiv:2603.16077v1 Announce Type: new Abstract: Masked diffusion models (MDM) exhibit superior generalization when learned using a Partial masking scheme (Prime). This approach converts tokens into sub-tokens and models the diffusion process at the sub-token level. We identify two limitations of...
The Supreme Court of Canada
Welcome to SCOTUSblog’s recurring series in which we interview experts on different supreme courts around the world and how they compare to our own. For our debut column, we covered […]The postThe Supreme Court of Canadaappeared first onSCOTUSblog.
The biggest names on the briefs
Empirical SCOTUS is a recurring series by Adam Feldman that looks at Supreme Court data, primarily in the form of opinions and oral arguments, to provide insights into the justices’ decision making and […]The postThe biggest names on the briefsappeared...
SCOTUStoday for Tuesday, March 17
Happy St. Patrick’s Day! We recommend celebrating by reading about Supreme Court justices of Irish descent.The postSCOTUStoday for Tuesday, March 17appeared first onSCOTUSblog.
Formulating Public Pharma
In 2022, prices for both brand-name and generic drugs in the United States were nearly three times as high as prices in comparably industrialized nations, with the cost of insulin products in particular being nearly ten times as high. As...
From Refusal Tokens to Refusal Control: Discovering and Steering Category-Specific Refusal Directions
arXiv:2603.13359v1 Announce Type: new Abstract: Language models are commonly fine-tuned for safety alignment to refuse harmful prompts. One approach fine-tunes them to generate categorical refusal tokens that distinguish different refusal types before responding. In this work, we leverage a version...
TheraAgent: Multi-Agent Framework with Self-Evolving Memory and Evidence-Calibrated Reasoning for PET Theranostics
arXiv:2603.13676v1 Announce Type: new Abstract: PET theranostics is transforming precision oncology, yet treatment response varies substantially; many patients receiving 177Lu-PSMA radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC) fail to respond, demanding reliable pre-therapy prediction. While LLM-based agents have...
Automating Document Intelligence in Statutory City Planning
arXiv:2603.13245v1 Announce Type: new Abstract: UK planning authorities face a legislative conflict between the Planning Act, which mandates public access to application documents, and the Data Protection Act, which requires protection of personal information. This situation creates a manually intensive...
Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction
arXiv:2603.13777v1 Announce Type: new Abstract: Aspect-based sentiment analysis (ABSA) extracts aspect-level sentiment signals from user-generated text, supports product analytics, experience monitoring, and public-opinion tracking, and is central to fine-grained opinion mining. A key challenge in ABSA is aspect sentiment quad...
The AI Fiction Paradox
arXiv:2603.13545v1 Announce Type: new Abstract: AI development has a fiction dependency problem: models are built on massive corpora of modern fiction and desperately need more of it, yet they struggle to generate it. I term this the AI-Fiction Paradox and...
MESD: Detecting and Mitigating Procedural Bias in Intersectional Groups
arXiv:2603.13452v1 Announce Type: new Abstract: Research about bias in machine learning has mostly focused on outcome-oriented fairness metrics (e.g., equalized odds) and on a single protected category. Although these approaches offer great insight into bias in ML, they provide limited...
How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing
arXiv:2603.13259v1 Announce Type: new Abstract: When a language model is fed a wrong answer, what happens inside the network? Current understanding treats truthfulness as a static property of individual-layer representations-a direction to be probed, a feature to be extracted. Less...
Why Grokking Takes So Long: A First-Principles Theory of Representational Phase Transitions
arXiv:2603.13331v1 Announce Type: new Abstract: Grokking is the sudden generalization that appears long after a model has perfectly memorized its training data. Although this phenomenon has been widely observed, there is still no quantitative theory explaining the length of the...
Widespread Gender and Pronoun Bias in Moral Judgments Across LLMs
arXiv:2603.13636v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to assess moral or ethical statements, yet their judgments may reflect social and linguistic biases. This work presents a controlled, sentence-level study of how grammatical person, number, and...
Deep Convolutional Architectures for EEG Classification: A Comparative Study with Temporal Augmentation and Confidence-Based Voting
arXiv:2603.13261v1 Announce Type: new Abstract: Electroencephalography (EEG) classification plays a key role in brain-computer interface (BCI) systems, yet it remains challenging due to the low signal-to-noise ratio, temporal variability of neural responses, and limited data availability. In this paper, we...
Multi-Axis Trust Modeling for Interpretable Account Hijacking Detection
arXiv:2603.13246v1 Announce Type: new Abstract: This paper proposes a Hadith-inspired multi-axis trust modeling framework, motivated by a structurally analogous problem in classical Hadith scholarship: assessing the trustworthiness of information sources using interpretable, multidimensional criteria rather than a single anomaly score....
SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions
arXiv:2603.14027v1 Announce Type: new Abstract: Political speakers often avoid answering questions directly while maintaining the appearance of responsiveness. Despite its importance for public discourse, such strategic evasion remains underexplored in Natural Language Processing. We introduce SemEval-2026 Task 6, CLARITY, a...
Mind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models
arXiv:2603.14313v1 Announce Type: new Abstract: Federal Open Market Committee (FOMC) statements are a major source of monetary-policy information, and even subtle changes in their wording can move global financial markets. A central task is therefore to measure the hawkish--dovish stance...
RFX-Fuse: Breiman and Cutler's Unified ML Engine + Native Explainable Similarity
arXiv:2603.13234v1 Announce Type: new Abstract: Breiman and Cutler's original Random Forest was designed as a unified ML engine -- not merely an ensemble predictor. Their implementation included classification, regression, unsupervised learning, proximity-based similarity, outlier detection, missing value imputation, and visualization...
Knowledge, Rules and Their Embeddings: Two Paths towards Neuro-Symbolic JEPA
arXiv:2603.13265v1 Announce Type: new Abstract: Modern self-supervised predictive architectures excel at capturing complex statistical correlations from high-dimensional data but lack mechanisms to internalize verifiable human logic, leaving them susceptible to spurious correlations and shortcut learning. Conversely, traditional rule-based inference systems...
Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design
arXiv:2603.13284v1 Announce Type: new Abstract: In this paper, we generate conceptual engineering designs of electric vertical take-off and landing (eVTOL) aircraft. We follow the paradigm of simulation-based inference (SBI), whereby we look to learn a posterior distribution over the full...
Pragma-VL: Towards a Pragmatic Arbitration of Safety and Helpfulness in MLLMs
arXiv:2603.13292v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) pose critical safety challenges, as they are susceptible not only to adversarial attacks such as jailbreaking but also to inadvertently generating harmful content for benign users. While internal safety alignment...
A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning
arXiv:2603.13293v1 Announce Type: new Abstract: Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models is hindered by the fragmentation of clinical data across institutions due to stringent privacy regulations. This paper...
ICPRL: Acquiring Physical Intuition from Interactive Control
arXiv:2603.13295v1 Announce Type: new Abstract: VLMs excel at static perception but falter in interactive reasoning in dynamic physical environments, which demands planning and adaptation to dynamic outcomes. Existing physical reasoning methods often depend on abstract symbolic inputs or lack the...
RBF-Solver: A Multistep Sampler for Diffusion Probabilistic Models via Radial Basis Functions
arXiv:2603.13330v1 Announce Type: new Abstract: Diffusion probabilistic models (DPMs) are widely adopted for their outstanding generative fidelity, yet their sampling is computationally demanding. Polynomial-based multistep samplers mitigate this cost by accelerating inference; however, despite their theoretical accuracy guarantees, they generate...
A Hierarchical End-of-Turn Model with Primary Speaker Segmentation for Real-Time Conversational AI
arXiv:2603.13379v1 Announce Type: new Abstract: We present a real-time front-end for voice-based conversational AI to enable natural turn-taking in two-speaker scenarios by combining primary speaker segmentation with hierarchical End-of-Turn (EOT) detection. To operate robustly in multi-speaker environments, the system continuously...