7 papers
When Should a Robot Think? Resource-Aware Reasoning via Reinforcement Learning for Embodied Robotic Decision-Making
Jun Liu, Pu Zhao, Zhenglun Kong +12
Embodied robotic systems increasingly rely on large language model (LLM)-based agents to support high-level reasoning, planning, and decision-making during interactions with the en…
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…
Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment
Jun Liu, Zhenglun Kong, Pu Zhao +9
Structured pruning for large language models (LLMs) has garnered significant academic interest due to its ability to efficiently compress and accelerate LLMs by eliminating redunda…
Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning
Qitao Tan, Jun Liu, Zheng Zhan +6
Large language models (LLMs) excel across various tasks, but standard first-order (FO) fine-tuning demands considerable memory, significantly limiting real-world deployment. Recent…
Brain Tumor Classification on MRI in Light of Molecular Markers
Jun Liu, Geng Yuan, Weihao Zeng +6
In research findings, co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. The ability to predict 1p19q status is critical for treatment planni…
RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory
Jun Liu, Zhenglun Kong, Changdi Yang +12
Multi-agent large language model (LLM) systems have shown strong potential in complex reasoning and collaborative decision-making tasks. However, most existing coordination schemes…