6 papers · 1 filter
ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents
Zihang Tian, Jingsen Zhang, Rui Li +3
Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or bad. Rubric-based rewards impro…
NextMem: Towards Latent Factual Memory for LLM-based Agents
Zeyu Zhang, Rui Li, Xiaoyan Zhao +4
Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational part. However, existing approaches…
Towards Adaptive, Scalable, and Robust Coordination of LLM Agents: A Dynamic Ad-Hoc Networking Perspective
Rui Li, Zeyu Zhang, Xiaohe Bo +4
Multi-agent architectures built on large language models (LLMs) have demonstrated the potential to realize swarm intelligence through well-crafted collaboration. However, the subst…
Explicit v.s. Implicit Memory: Exploring Multi-hop Complex Reasoning Over Personalized Information
Zeyu Zhang, Yang Zhang, Haoran Tan +2
In large language model-based agents, memory serves as a critical capability for achieving personalization by storing and utilizing users' information. Although some previous studi…
MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
Zeyu Zhang, Quanyu Dai, Xu Chen +3
Recently, large language model based (LLM-based) agents have been widely applied across various fields. As a critical part, their memory capabilities have captured significant inte…
MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
Zeyu Zhang, Quanyu Dai, Luyu Chen +7
LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries. However, there still lack…