arXiv:2510.11194v2 Announce Type: replace Abstract: Personalized alignment is crucial for enabling Large Language Models (LLMs) to engage effectively in user-centric interactions. However, current methods face a dual challenge: they fail to infer users’ deep implicit preferences (including unstated goals, semantic context and risk tolerances), and they lack the defensive reasoning required to navigate real-world ambiguity. […]
The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
arXiv:2604.14807v2 Announce Type: replace Abstract: The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and multilingual communication. While prior research has focused on model reliability, hallucination, and user trust calibration, less attention has been given to how LLM usage reshapes users’ […]
Origin-Destination Demand Prediction: An Urban Radiation and Attraction Perspective
arXiv:2412.00167v2 Announce Type: replace-cross Abstract: In recent years, origin-destination (OD) demand prediction has gained significant attention for its profound implications in urban development. Existing data-driven deep learning methods primarily focus on the spatial or temporal dependency between regions yet neglecting regions’ fundamental functional difference. Though knowledge-driven physical methods have characterised regions’ functions by their radiation […]
Named Entity Recognition of Historical Texts via Large Language Model
arXiv:2508.18090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. One such task is Named Entity Recognition (NER), which involves identifying and classifying proper names in text, such as people, organizations, locations, dates, and other specific entities. NER plays a crucial […]
Physics-Informed Neural Networks for Nonlinear Output Regulation
arXiv:2511.13595v3 Announce Type: replace-cross Abstract: This work addresses the full-information output regulation problem for nonlinear systems, assuming the states of both the plant and the exosystem are known. In this setting, perfect tracking or rejection is achieved by constructing a zero-regulation-error manifold $pi(w)$ and a feedforward input $c(w)$ that render such manifold invariant. The pair […]
Evaluating LLM Safety Under Repeated Inference via Accelerated Prompt Stress Testing
arXiv:2602.11786v2 Announce Type: replace-cross Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety through breadth-oriented evaluation across diverse tasks and risk categories. However, real-world deployment often exposes a different class of risk: operational failures that arise under repeated inference on identical or near-identical prompts rather than from broad […]
ClawEnvKit: Automatic Environment Generation for Claw-Like Agents
arXiv:2604.18543v3 Announce Type: replace Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale. We argue that what is needed is not just a dataset, but an automated pipeline capable of generating diverse, verified environments on demand. To this end, we introduce ClawEnvKit, an autonomous generation pipeline […]
Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers
npj Digital Medicine, Published online: 29 April 2026; doi:10.1038/s41746-026-02686-3 Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers
Reinforcement learning based automated anesthesia system for gastrointestinal endoscopy with a multicenter randomized trial
npj Digital Medicine, Published online: 29 April 2026; doi:10.1038/s41746-026-02657-8 Reinforcement learning based automated anesthesia system for gastrointestinal endoscopy with a multicenter randomized trial