Video large language models (VideoLLMs) achieve strong video understanding performance, but their inference remains expensive due to the large number of redundant spatio-temporal visual tokens in long videos. Existing token pruning methods alleviate this cost by reducing redundant tokens, yet most of them rely on segme...
Meng-Jie Zhang, Qi-Hui Zhu, Tao Zhang et al.· 0 citations
LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication. However, directly forwarding all sender latents makes the receiver-side context scale with both the number of agents and the reasoning length...
Shi-Nan Zhang, Tao Zhang, Qi-Hui Zhu et al.· 0 citations
It is demonstrated that resolution differences can serve as a simple and scalable source of privileged information, providing an effective and efficient approach to on-policy self-distillation for multimodal large language models.
It is observed that attention scores from both vision and text tokens peak at modality separator tokens, suggesting that these separators bridge the two modalities and proposes SepPrune, an efficient, training-free, plug-and-play pruning method that uses the separator token as a unified query to rank and select informa...
Yucheng Wang, Qihui Zhu, Yang Liu et al.· arXiv.org· 0 citations
Experiments across three instruction-tuned models show that HiRoute achieves high safety rates across multiple safety benchmarks while preserving safe-response helpfulness, reducing over-refusal, and maintaining competitive performance on general-purpose tasks.
Fang-Zhou Chen, Shiji Zhao, Mengyan Wang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
