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:2401.02458v3 Announce Type: replace-cross
Abstract: The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre…
arXiv:2604.26181v1 Announce Type: new
Abstract: Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platform resources. Current n…
arXiv:2604.25530v2 Announce Type: replace-cross
Abstract: Recent knowledge distillation (KD) methods for semantic segmentation introduce increasingly complex hand-crafted objectives, yet are typically evaluated under fixed iteration schedules. These o…
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:2604.26091v1 Announce Type: new
Abstract: We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX Terminal Pro, a 21-day deployment in which 3,505 us…
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…
arXiv:2604.26256v1 Announce Type: new
Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase — accounting for 50–80% of total step time — is bottlenecked by skewed generation: long-tailed t…