1.6k citations · 1.6k across the 12 of their papers we have counts for
14 papers
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
Xixuan Hao, Zeyu Zhang, Zehao Lin +6
Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions. Existing benchmarks, however, evaluate…
CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension
Rui Li, Zeyu Zhang, Xiaohe Bo +5
Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive…
Prompt and Parameter Co-Optimization for Large Language Models
Xiaohe Bo, Rui Li, Zexu Sun +5
Prompt optimization and fine-tuning are two major approaches to improve the performance of Large Language Models (LLMs). They enhance the capabilities of LLMs from complementary pe…
Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework
Zeyu Zhang, Quanyu Dai, Rui Li +3
LLM-based agents have been extensively applied across various domains, where memory stands out as one of their most essential capabilities. Previous memory mechanisms of LLM-based…
RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
Luyu Chen, Quanyu Dai, Zeyu Zhang +6
Conversational recommender systems (CRS) enhance user experience through multi-turn interactions, yet evaluating CRS remains challenging. User simulators can provide comprehensive…
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents
Haoran Tan, Zeyu Zhang, Chen Ma +3
Recent works have highlighted the significance of memory mechanisms in LLM-based agents, which enable them to store observed information and adapt to dynamic environments. However,…
KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing
Rui Li, Quanyu Dai, Zeyu Zhang +3
Recent advances in retrieval-augmented generation (RAG) furnish large language models (LLMs) with iterative retrievals of relevant information to handle complex multi-hop questions…
Beyond Single-Point Judgment: Distribution Alignment for LLM-as-a-Judge
Luyu Chen, Zeyu Zhang, Haoran Tan +4
LLMs have emerged as powerful evaluators in the LLM-as-a-Judge paradigm, offering significant efficiency and flexibility compared to human judgments. However, previous methods prim…
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…
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System
Zeyu Zhang, Jianxun Lian, Chen Ma +9
Trending topics have become a significant part of modern social media, attracting users to participate in discussions of breaking events. However, they also bring in a new channel…
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…
A Survey on the Memory Mechanism of Large Language Model based Agents
Zeyu Zhang, Xiaohe Bo, Chen Ma +6
Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are feature…
A Survey on Large Language Model based Autonomous Agents
Lei Wang, Chen Ma, Xueyang Feng +10
Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited…
User Behavior Simulation with Large Language Model based Agents
Lei Wang, Jingsen Zhang, Hao Yang +11
Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism…