arXiv:2510.07517v1 Announce Type: new Abstract: Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their opinions. Yet recent studies reveal that agents are not neutral: they are prone to…
arXiv:2509.13499v2 Announce Type: replace-cross Abstract: Online artificial intelligence (AI) algorithms are an important component of digital health interventions. These online algorithms are designed to continually learn and improve their performance as streaming data is collected on individuals. Deploying online AI…
arXiv:2510.07516v1 Announce Type: new Abstract: The popular path query – identifying the most frequented routes between locations from historical trajectory data – has important applications in urban planning, navigation optimization, and travel recommendations. While traditional algorithms and machine learning approaches…
arXiv:2510.07491v1 Announce Type: new Abstract: Medical Intelligent Systems (MIS) are increasingly integrated into healthcare workflows, offering significant benefits but also raising critical safety and ethical concerns. According to the European Union AI Act, most MIS will be classified as high-risk…
arXiv:2503.18156v3 Announce Type: replace-cross Abstract: AI-generated images have become so good in recent years that individuals often cannot distinguish them any more from “real” images. This development, combined with the rapid spread of AI-generated content online, creates a series of…
arXiv:2510.07489v1 Announce Type: new Abstract: In this paper, we report our experience with several LLMs for their ability to understand a process model in an interactive, conversational style, find syntactical and logical errors in it, and reason with it in…
arXiv:2411.02886v4 Announce Type: replace-cross Abstract: Rapid advances in Large Language Models (LLMs) have spurred demand for processing extended context sequences in contemporary applications. However, this progress faces two challenges: performance degradation due to sequence lengths out-of-distribution, and excessively long inference…
arXiv:2510.07364v1 Announce Type: new Abstract: Why do thinking language models like DeepSeek R1 outperform their base counterparts? Despite consistent performance gains, it remains unclear to what extent thinking models learn entirely new reasoning capabilities or repurpose pre-existing base model ones.…
arXiv:2510.07409v1 Announce Type: new Abstract: Static solutions don’t serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore…
arXiv:2510.07423v1 Announce Type: new Abstract: Large language models (LLMs) have empowered AI agents to tackle increasingly complex tasks. However, most existing agents remain limited to static planning and brittle interactions, falling short of true collaboration or adaptive reasoning. We introduce…
