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arXiv:2603.10291v1 Announce Type: new Abstract: The remarkable progress of vision-language models (VLMs) has enabled GUI agents to interact with computers in a human-like manner. Yet …
arXiv:2603.10008v1 Announce Type: cross Abstract: This paper presents system description for Arabic medical text classification across 82 distinct categories. Our primary architecture utilizes a fine-tuned …
arXiv:2603.10010v1 Announce Type: cross Abstract: We introduce a multi-faceted automated red teaming framework in which the goal is to generate multi-modal adversarial conversations that would …
arXiv:2603.09992v1 Announce Type: cross Abstract: This paper presents TAMUSA-Chat, a research-oriented framework for building domain-adapted large language model conversational systems. The work addresses critical challenges …
arXiv:2603.09989v1 Announce Type: cross Abstract: We introduce the System Hallucination Scale (SHS), a lightweight and human-centered measurement instrument for assessing hallucination-related behavior in large language …
arXiv:2603.09988v1 Announce Type: cross Abstract: Mechanistic interpretability identifies internal circuits responsible for model behaviors, yet translating these findings into human-understandable explanations remains an open problem. …
arXiv:2603.10600v1 Announce Type: new Abstract: LLM-powered agents face a persistent challenge: learning from their execution experiences to improve future performance. While agents can successfully complete …
arXiv:2603.10512v1 Announce Type: new Abstract: Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for decision-making, strategic planning, and …
arXiv:2603.10005v1 Announce Type: cross Abstract: Many Automatic Speech Recognition (ASR) applications require streaming processing of the audio data. In streaming mode, ASR systems need to …
arXiv:2603.09985v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet their ability to accurately assess their own confidence …
arXiv:2603.10577v1 Announce Type: new Abstract: Computer-Use Agents (CUAs) are emerging as a new paradigm in human-computer interaction, enabling autonomous execution of tasks in desktop environment …
arXiv:2603.10016v1 Announce Type: cross Abstract: We investigate whether large language models (LLMs) display human-like cognitive biases, focusing on potential implications for assistance in judicial sentencing, …