arXiv:2604.03444v2 Announce Type: replace-cross
Abstract: Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attention. Yet there …
arXiv:2601.11652v2 Announce Type: replace-cross
Abstract: As Large Language Models (LLMs) become increasingly accessible to end users, an ever-growing number of inference requests are initiated from edge devices and computed on centralized GPU cluster…
arXiv:2604.05360v1 Announce Type: cross
Abstract: Gait analysis is essential in post-stroke rehabilitation but remains time-intensive and cognitively demanding, especially when clinicians must integrate gait videos and motion-capture data into structu…
arXiv:2603.14093v3 Announce Type: replace-cross
Abstract: As modern text-to-image (T2I) models draw closer to synthesizing highly realistic content, the threat of unsafe content generation grows, and it becomes paramount to exercise control. Existing …
arXiv:2603.23448v3 Announce Type: replace-cross
Abstract: Software engineering agents have shown significant promise in writing code. As AI agents permeate code writing, and generate huge volumes of code automatically — the matter of code quality com…
arXiv:2604.02623v2 Announce Type: replace-cross
Abstract: Memory makes LLM-based web agents personalized, powerful, yet exploitable. By storing past interactions to personalize future tasks, agents inadvertently create a persistent attack surface that…
arXiv:2604.05350v1 Announce Type: new
Abstract: Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user reports to identify an underlying root cause. While retriev…
arXiv:2604.05477v1 Announce Type: new
Abstract: Autonomous GUI agents based on vision-language models (VLMs) often assume deterministic environment responses, generating actions without verifying whether previous operations succeeded. In real-world se…
arXiv:2604.05522v1 Announce Type: new
Abstract: Omni Large Language Models (Omni-LLMs) have demonstrated impressive capabilities in holistic multi-modal perception, yet they consistently falter in complex scenarios requiring synergistic omni-modal rea…
arXiv:2604.05546v1 Announce Type: new
Abstract: Large Vision-Language Models (LVLMs) enable sophisticated reasoning over images and videos, yet their inference is hindered by a systemic efficiency barrier known as visual token dominance. This overhead…