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arXiv:2603.10808v1 Announce Type: new Abstract: The emergence of large language model (LLM)-based agent frameworks has shifted the primary challenge in building domain-expert AI agents from …
arXiv:2603.10009v1 Announce Type: cross Abstract: Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training …
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:2603.10661v1 Announce Type: new Abstract: We propose FAME (Formal Abstract Minimal Explanations), a new class of abductive explanations grounded in abstract interpretation. FAME is the …
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:2603.09982v1 Announce Type: cross Abstract: Encoder-only transformer models remain widely used for discriminative NLP tasks, yet recent architectural advances have largely focused on English. In …
arXiv:2603.09997v1 Announce Type: cross Abstract: When OpenAI deprecated GPT-4o in early 2026, thousands of users protested under #keep4o, claiming newer models had "lost their empathy." …
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:2603.10018v1 Announce Type: cross Abstract: As large language models (LLMs) become pervasive as assistants and thought partners, it is important to characterize their persuasive influence …
arXiv:2603.09995v1 Announce Type: cross Abstract: Behavioral interview evaluation using large language models presents unique challenges that require structured assessment, realistic interviewer behavior simulation, and pedagogical …
arXiv:2603.10384v1 Announce Type: new Abstract: Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework …
arXiv:2603.10002v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly tasked with producing and manipulating structured artifacts. We consider the task of end-to-end spreadsheet …