11 citations · 14 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…