6 papers
AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents
Wenbo Gao, Renxi Liu, Xian Wang +8
Autonomous agents powered by large language models (LLMs) perform complex tasks through long-horizon reasoning and tool interaction, where a fundamental trade-off arises between ex…
DLLM Agent: See Farther, Run Faster
Huiling Zhen, Weizhe Lin, Renxi Liu +15
Diffusion large language models (DLLMs) have emerged as an alternative to autoregressive (AR) decoding with appealing efficiency and modeling properties, yet their implications for…
Towards Efficient Agents: A Co-Design of Inference Architecture and System
Weizhe Lin, Hui-Ling Zhen, Shuai Yang +14
The rapid development of large language model (LLM)-based agents has unlocked new possibilities for autonomous multi-turn reasoning and tool-augmented decision-making. However, the…
EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization
Zhongqian Fu, Tianyi Zhao, Ning Ding +4
Mixture-of-Experts (MoE) models enable scalable computation and performance in large-scale deep learning but face quantization challenges due to sparse expert activation and dynami…
TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling
Weizhe Lin, Xing Li, Zhiyuan Yang +7
Large Reasoning Models (LRMs) demonstrate exceptional capability in tackling complex mathematical, logical, and coding tasks by leveraging extended Chain-of-Thought (CoT) reasoning…
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
Yehui Tang, Xiaosong Li, Fangcheng Liu +19
The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because on…