arXiv:2604.26328v1 Announce Type: new
Abstract: The rapid advancement of large language models (LLMs) presents new security challenges, particularly in detecting machine-generated text used for misinformation, impersonation, and content forgery. Most …
arXiv:2412.13682v5 Announce Type: replace-cross
Abstract: Travel planning stands out among real-world applications of \emph{Language Agents} because it couples significant practical demand with a rigorous constraint-satisfaction challenge. However, ex…
arXiv:2602.23163v3 Announce Type: replace-cross
Abstract: Large language models are beginning to show steganographic capabilities. Such capabilities could allow misaligned models to evade oversight mechanisms. Yet principled methods to detect and quan…
arXiv:2604.26412v1 Announce Type: new
Abstract: Speculative decoding accelerates LLM inference, but SOTA hidden-state-based drafters suffer from long-range decay: draft accuracy degrades as the speculative step increases. Existing work attributes this…
arXiv:2409.00557v4 Announce Type: replace
Abstract: Equipped with the capability to call functions, modern large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone. However, th…
arXiv:2604.26500v1 Announce Type: new
Abstract: LLMs and speech assistants are increasingly used for task-oriented interactions, yet their evaluation often relies on controlled scenarios that fail to capture the variability and complexity of real user…
arXiv:2502.11614v3 Announce Type: replace
Abstract: Prior studies have shown that distinguishing text generated by Large Language Models (LLMs) from human-written one is highly challenging for humans, and often no better than random guessing. To verif…
arXiv:2503.04872v3 Announce Type: replace
Abstract: The challenge of reducing the size of Large Language Models (LLMs) while maintaining their performance has gained significant attention. However, existing methods, such as model distillation and tran…
arXiv:2604.26506v1 Announce Type: new
Abstract: As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts — adversarial instructions embedded in submissions to manipulate outcome…
arXiv:2505.13963v3 Announce Type: replace
Abstract: Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization’s effects on various LLM capabilities have been exten…