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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 …
arXiv:2603.04409v1 Announce Type: new Abstract: The evaluation of large language models faces significant challenges. Technical benchmarks often lack real-world relevance, while existing human preference evaluations …
arXiv:2603.04410v1 Announce Type: new Abstract: Safety alignment in Language Models (LMs) is fundamental for trustworthy AI. However, while different stakeholders are trying to leverage Arabic …
arXiv:2603.04411v1 Announce Type: new Abstract: Despite the remarkable progress of Large Language Models (LLMs), the escalating memory footprint of the Key-Value (KV) cache remains a …
arXiv:2603.04412v1 Announce Type: new Abstract: Large-scale language models (LLMs) operate in extremely high-dimensional state spaces, where both token embeddings and their hidden representations create complex …
arXiv:2603.04413v1 Announce Type: new Abstract: Meaning in human language is relational, context dependent, and emergent, arising from dynamic systems of signs rather than fixed word-concept …