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arXiv:2603.05027v1 Announce Type: new Abstract: The smart home is a key application domain within the Society 5.0 vision for a human-centered society. As smart home …
arXiv:2603.05028v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve from chatbots to agentic assistants, they are increasingly observed to exhibit risky behaviors when …
arXiv:2603.05031v1 Announce Type: new Abstract: AI agents that build user interfaces on the fly assembling buttons, forms, and data displays from structured protocol payloads are …
arXiv:2603.05036v1 Announce Type: new Abstract: This paper introduces the "Trilingual Triad" framework, a model that explains how students learn to design with generative artificial intelligence …
arXiv:2603.05040v1 Announce Type: new Abstract: Recent advancements in zero-shot commonsense reasoning have empowered Pre-trained Language Models (PLMs) to acquire extensive commonsense knowledge without requiring task-specific …
arXiv:2603.05044v1 Announce Type: new Abstract: Current paradigms for training GUI agents are fundamentally limited by a reliance on either unsafe, non-reproducible live web interactions or …
arXiv:2603.05069v1 Announce Type: new Abstract: Personal AI agents face a fundamental deployment paradox on mobile: persistent background execution drains battery and violates platform sandboxing policies, …
arXiv:2603.05120v1 Announce Type: new Abstract: Enhancing mathematical reasoning in Large Language Models typically demands massive datasets, yet data efficiency remains a critical bottleneck. While Curriculum …
arXiv:2603.05129v1 Announce Type: new Abstract: Diagnosing hepatic diseases accurately and interpretably is critical, yet it remains challenging in real-world clinical settings. Existing AI approaches for …
arXiv:2603.04406v1 Announce Type: new Abstract: With the growing use of Retrieval-Augmented Generation (RAG), training large language models (LLMs) for context-sensitive reasoning and faithfulness is increasingly …
arXiv:2603.04407v1 Announce Type: new Abstract: Fine-tuning language models on narrowly harmful data causes emergent misalignment (EM) -- behavioral failures extending far beyond training distributions. Recent …
arXiv:2603.04408v1 Announce Type: new Abstract: Current evaluation paradigms for large language models (LLMs) characterize models and datasets separately, yielding coarse descriptions: items in datasets are …