3 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory
Jun Liu, Zhenglun Kong, Changdi Yang +12
Large language model (LLM) agents are increasingly organized into multi-agent systems for complex reasoning and decision-making tasks. However, scaling such systems can substantial…
TSLA: A Task-Specific Learning Adaptation for Semantic Segmentation on Autonomous Vehicles Platform
Jun Liu, Zhenglun Kong, Pu Zhao +9
Autonomous driving platforms encounter diverse driving scenarios, each with varying hardware resources and precision requirements. Given the computational limitations of embedded d…
Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation
Zhenglun Kong, Zheng Zhan, Shiyue Hou +10
Large language models (LLMs) have shown remarkable promise but remain challenging to continually improve through traditional finetuning, particularly when integrating capabilities…
Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality
Zhenglun Kong, Yize Li, Fanhu Zeng +7
In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped…
Structured Agent Distillation for Large Language Model
Jun Liu, Zhenglun Kong, Peiyan Dong +10
Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-style frameworks. Yet, their practical de…
RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation
Jun Liu, Zhenglun Kong, Peiyan Dong +10
Fine-tuning helps large language models (LLM) recover degraded information and enhance task performance. Although Low-Rank Adaptation (LoRA) is widely used and effective for fine-t…