arXiv:2604.07535v1 Announce Type: new Abstract: Studies show that interactions with an AI system fosters trust in human users towards AI. An often overlooked element of such interaction dynamics is the (sense of) urgency when the human user is prompted by an AI agent, e.g., for advice or guidance. In this paper, we show that although […]
Hybrid CNN-Transformer Architecture for Arabic Speech Emotion Recognition
arXiv:2604.07357v1 Announce Type: cross Abstract: Recognizing emotions from speech using machine learning has become an active research area due to its importance in building human-centered applications. However, while many studies have been conducted in English, German, and other European and Asian languages, research in Arabic remains scarce because of the limited availability of annotated datasets. […]
Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction
arXiv:2604.07964v1 Announce Type: new Abstract: The integration of large language models (LLMs) into peer review raises a concern beyond authorship and detection: the potential cascading automation of the entire editorial process. As reviews become partially or fully machine-generated, it becomes plausible that editorial decisions may also be delegated to algorithmic systems, leading to a fully […]
Wiring the ‘Why’: A Unified Taxonomy and Survey of Abductive Reasoning in LLMs
arXiv:2604.08016v1 Announce Type: new Abstract: Regardless of its foundational role in human discovery and sense-making, abductive reasoning–the inference of the most plausible explanation for an observation–has been relatively underexplored in Large Language Models (LLMs). Despite the rapid advancement of LLMs, the exploration of abductive reasoning and its diverse facets has thus far been disjointed rather […]
An Agentic Evaluation Architecture for Historical Bias Detection in Educational Textbooks
arXiv:2604.07883v1 Announce Type: new Abstract: History textbooks often contain implicit biases, nationalist framing, and selective omissions that are difficult to audit at scale. We propose an agentic evaluation architecture comprising a multimodal screening agent, a heterogeneous jury of five evaluative agents, and a meta-agent for verdict synthesis and human escalation. A central contribution is a […]
SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking
arXiv:2604.07922v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive “overthinking”, generating unnecessarily long reasoning chains. While current solutions improve token efficiency, they often sacrifice fine-grained control or risk disrupting the logical integrity of the reasoning process. To address this, we introduce Stepwise Adaptive Thinking (SAT), a […]
Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
arXiv:2604.08401v1 Announce Type: new Abstract: In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory. However, coherent reasoning can still violate logical or evidential constraints, allowing unsupported beliefs repeatedly stored and propagated across decision steps, leading to systematic behavioral drift in long-horizon agentic systems. Most […]
ACF: A Collaborative Framework for Agent Covert Communication under Cognitive Asymmetry
arXiv:2604.08276v1 Announce Type: new Abstract: As generative artificial intelligence evolves, autonomous agent networks present a powerful paradigm for interactive covert communication. However, because agents dynamically update internal memories via environmental interactions, existing methods face a critical structural vulnerability: cognitive asymmetry. Conventional approaches demand strict cognitive symmetry, requiring identical sequence prefixes between the encoder and decoder. […]
Time-Varying Environmental and Polygenic Predictors of Substance Use Initiation in Youth: A Survival and Causal Modeling Study in the ABCD Cohort
arXiv:2604.07368v1 Announce Type: new Abstract: Early initiation of alcohol, nicotine, cannabis, and other substances predicts later substance use disorders and related psychopathology. We integrate time-varying environmental factors with polygenic risk scores (PRS) in a longitudinal framework to identify determinants of substance initiation in adolescence. Using data from the Adolescent Brain Cognitive Development (ABCD) Study with […]
Revise: A Framework for Revising OCRed text in Practical Information Systems with Data Contamination Strategy
arXiv:2604.08115v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have significantly improved the field of Document AI, demonstrating remarkable performance on document understanding tasks such as question answering. However, existing approaches primarily focus on solving specific tasks, lacking the capability to structurally organize and manage document information. To address this limitation, we […]
Grounding Clinical AI Competency in Human Cognition Through the Clinical World Model and Skill-Mix Framework
arXiv:2604.08226v1 Announce Type: new Abstract: The competency of any intelligent agent is bounded by its formal account of the world in which it operates. Clinical AI lacks such an account. Existing frameworks address evaluation, regulation, or system design in isolation, without a shared model of the clinical world to connect them. We introduce the Clinical […]
ASPECT:Analogical Semantic Policy Execution via Language Conditioned Transfer
arXiv:2604.08355v1 Announce Type: new Abstract: Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered. Although recent approaches have attempted to mitigate this issue via zero-shot transfer, they are often constrained by predefined, discrete class systems, limiting their adaptability to novel or compositional task […]