5 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents
Ziyan Liu, Zhezheng Hao, Yeqiu Chen +7
Memory-augmented LLM agents tackle complex long-horizon tasks by recursively summarizing interaction trajectories into compact memory. However, existing approaches typically train…
ReCreate: Reasoning and Creating Domain Agents Driven by Experience
Zhezheng Hao, Hong Wang, Jian Luo +6
Large Language Model agents are reshaping the industrial landscape. However, most practical agents remain human-designed because tasks differ widely, making them labor-intensive to…
Scheduling Your LLM Reinforcement Learning with Reasoning Trees
Hong Wang, Zhezheng Hao, Jian Luo +6
Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing a query's `Reasoning Tree'. This…
Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective
Ritik Raj, Souvik Kundu, Ishita Vohra +2
Agentic AI serving converts monolithic LLM-based inference to autonomous problem-solvers that can plan, call tools, perform reasoning, and adapt on the fly. Due to diverse task exe…
EDIT: Early Diffusion Inference Termination for dLLMs Based on Dynamics of Training Gradients
He-Yen Hsieh, Hong Wang, H. T. Kung
Diffusion-based large language models (dLLMs) refine token generations through iterative denoising, but answers often stabilize before all steps complete. We propose EDIT (Early Di…