Quality follows upgrading
Tag: cs.HC
arXiv:2604.00477v1 Announce Type: new Abstract: LLM-based agent judges are an emerging approach to evaluating conversational AI, yet a fundamental uncertainty remains: can we trust their …
arXiv:2604.01653v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive insight into the brain's cognitive and emotional dynamics. However, modeling how these states evolve in …
arXiv:2603.23526v1 Announce Type: new Abstract: The growing publication rate of research papers has created an urgent need for better ways to fact-check information, assess writing …
arXiv:2603.23114v1 Announce Type: new Abstract: A human's moral decision depends heavily on the context. Yet research on LLM morality has largely studied fixed scenarios. We …
arXiv:2603.23406v1 Announce Type: new Abstract: While large language models simulate social behaviors, their capacity for stable stance formation and identity negotiation during complex interventions remains …
arXiv:2603.22935v1 Announce Type: new Abstract: Chest X-ray report generation and automated evaluation are limited by poor recognition of low-prevalence abnormalities and inadequate handling of clinically …
arXiv:2603.20255v1 Announce Type: new Abstract: Speech-based AI educational applications have gained significant interest in recent years, particularly for children. However, children speech research remains limited …
arXiv:2603.20381v1 Announce Type: new Abstract: Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe, …
arXiv:2603.20939v1 Announce Type: new Abstract: Large language models are increasingly used as personal assistants, yet most lack a persistent user model, forcing users to repeatedly …
arXiv:2603.21038v1 Announce Type: new Abstract: As text-based computer-mediated communication (CMC) increasingly structures everyday interaction, a central question re-emerges with new urgency: How do users reconstruct …
arXiv:2603.19263v1 Announce Type: cross Abstract: This study examined how high school students with different motivational profiles use generative AI tools in math and writing. Through …
arXiv:2603.19265v1 Announce Type: cross Abstract: This paper investigates the ontological consequences of fine-tuning Large Language Models (LLMs) on "impossible objects" -- entities defined by mutually …
