4 citations · 5 across the 6 of their papers we have counts for
8 papers
DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
DeepSeek-AI, :, Anyi Xu +585
The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation…
VAPO: End-to-end Slide-Enhanced Speech Recognition with Omni-modal Large Language Models
Rui Hu, Delai Qiu, Yining Wang +2
Omni-modal large language models (OLLMs) offer a promising end-to-end solution for slide-enhanced speech recognition due to their inherent multimodal capabilities. However, we foun…
CAVALRY-V: A Large-Scale Generator Framework for Adversarial Attacks on Video MLLMs
Jiaming Zhang, Rui Hu, Qing Guo +1
Video Multimodal Large Language Models (V-MLLMs) have shown impressive capabilities in temporal reasoning and cross-modal understanding, yet their vulnerability to adversarial atta…
Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models
Rui Hu, Delai Qiu, Shuyu Wei +4
Omnimodal Large Language Models (OLLMs) have shown significant progress in integrating vision and text, but still struggle with integrating vision and audio, often exhibiting subop…
ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models
Yahan Tu, Rui Hu, Jitao Sang
Hallucination poses a persistent challenge for multimodal large language models (MLLMs). However, existing benchmarks for evaluating hallucinations are generally static, which may…
Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
Rui Hu, Yahan Tu, Shuyu Wei +2
Despite achieving outstanding performance on various cross-modal tasks, current large vision-language models (LVLMs) still suffer from hallucination issues, manifesting as inconsis…
Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber
Rui Hu, Yahan Tu, Jitao Sang
Neural networks often learn spurious correlations when exposed to biased training data, leading to poor performance on out-of-distribution data. A biased dataset can be divided, ac…
Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data
Rui Hu, Jitao Sang, Jinqiang Wang +1
Deep network models perform excellently on In-Distribution (ID) data, but can significantly fail on Out-Of-Distribution (OOD) data. While developing methods focus on improving OOD…