3 papers indexed
Mingdai Yang, Zhiwei Liu, Weizhi Zhang, Yibo Wang, Hao Peng, Philip Yu
Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for f…
Yuanjie Zhu, Liangwei Yang, Ke Xu, Weizhi Zhang, Shanghao Li, Zihe Song, et al.
Efficient serving of diffusion large language models (dLLMs) is hindered by convergence heterogeneity: when batching multiple requests, different sequences converge at different rates, causing faster requests to stall behind slower stragglers and introducing compute bubbles and t…
Yaozu Wu, Wei-Chieh Huang, Jizhou Guo, Dongyuan Li, Renhe Jiang, Henry Peng Zou, et al.
Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework that represents humans and LLM-powered agents as first-class participants with explicit roles, perm…