5 citations · 8 across the 12 of their papers we have counts for
15 papers
EAT: Expert Account Tracker for Efficient MoE Inference
Yuexian Li, Yifei Yang, Zouying Cao +1
Mixture-of-Experts (MoE) models have emerged as a revolutionary method to scale Transformer models. However, traditional MoE architecture still suffers from inefficiency since a la…
On the Efficiency-Safety Dilemma in Large Reasoning Models
Yifei Yang, Zouying Cao, Xingrui Wang +4
Large reasoning models (LRMs) incur high inference costs, often mitigated by efficiency techniques like quantization and pruning. However, the impact of these techniques on model a…
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization
Shan He, Runze Wang, Zhuoyun Du +4
Designing and optimizing multi-agent systems (MAS) is a complex, labor-intensive process of "Agent Engineering." Existing automatic optimization methods, primarily focused on flat…
Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
Zouying Cao, Jiaji Deng, Li Yu +4
Procedural memory enables large language model (LLM) agents to internalize "how-to" knowledge, theoretically reducing redundant trial-and-error. However, existing frameworks predom…
AgentEvolver: Towards Efficient Self-Evolving Agent System
Yunpeng Zhai, Shuchang Tao, Cheng Chen +10
Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in d…
Enabling Agents to Communicate Entirely in Latent Space
Zhuoyun Du, Runze Wang, Huiyu Bai +6
While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling rich, internal latent states int…
ParaCook: On Time-Efficient Planning for Multi-Agent Systems
Shiqi Zhang, Xinbei Ma, Yunqing Xu +7
Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks, yet existing agent benchmarks focus on task completion while neglecting…
PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization
Zouying Cao, Runze Wang, Yifei Yang +4
Large Language Model (LLM) agents have demonstrated impressive capabilities in handling complex interactive problems. Existing LLM agents mainly generate natural language plans to…
Plan-over-Graph: Towards Parallelable LLM Agent Schedule
Shiqi Zhang, Xinbei Ma, Zouying Cao +2
Large Language Models (LLMs) have demonstrated exceptional abilities in reasoning for task planning. However, challenges remain under-explored for parallel schedules. This paper in…
LESA: Learnable LLM Layer Scaling-Up
Yifei Yang, Zouying Cao, Xinbei Ma +4
Training Large Language Models (LLMs) from scratch requires immense computational resources, making it prohibitively expensive. Model scaling-up offers a promising solution by leve…
KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing
Yifei Yang, Zouying Cao, Qiguang Chen +4
The development of large language models (LLMs) has significantly expanded model sizes, resulting in substantial GPU memory requirements during inference. The key and value storage…
SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering
Zouying Cao, Yifei Yang, Hai Zhao
Safety alignment is indispensable for Large Language Models (LLMs) to defend threats from malicious instructions. However, recent researches reveal safety-aligned LLMs prone to rej…
Head-wise Shareable Attention for Large Language Models
Zouying Cao, Yifei Yang, Hai Zhao
Large Language Models (LLMs) suffer from huge number of parameters, which restricts their deployment on edge devices. Weight sharing is one promising solution that encourages weigh…
LaCo: Large Language Model Pruning via Layer Collapse
Yifei Yang, Zouying Cao, Hai Zhao
Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion, which brings considerable costs to both model training and inference. However, e…
AutoHall: Automated Factuality Hallucination Dataset Generation for Large Language Models
Zouying Cao, Yifei Yang, XiaoJing Li +1
Large language models (LLMs) have gained broad applications across various domains but still struggle with hallucinations. Currently, hallucinations occur frequently in the generat…