aiqbench analysis
This research establishes a unified framework to systematically benchmark memory mechanisms in LLM-based agents, addressing the critical need for standardized evaluation across long-horizon tasks like multi-turn dialogue and scientific discovery. By comparing existing methods under identical experimental settings, the study not only identifies performance gaps but also introduces a novel modular architecture that outperforms current state-of-the-art approaches. These findings provide essential insights into optimizing knowledge accumulation and iterative reasoning capabilities within next-generation AI agents.
Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework
arXiv:2604.01707v3 Announce Type: replace Abstract: Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), where memory can enable knowledge accumulation, iterative reasoning and self-evolution. A number of memory methods have been proposed in the literature. However, these methods have not been systematically and comprehensively compared under the same experimental settings. In this paper, we first summarize a unified framework that covers existing representative agent memory methods from a high-level perspective. We then extensively compare representative agent memory methods on two long-term conversational benchmarks and an agentic memory benchmark, and examine the effectiveness of representative methods, providing a thorough analysis of those methods. As a byproduct of our experimental analysis, we also design a new memory method by exploiting modules in the existing methods, which outperforms the state-of-the-art methods. Finally, based on these findings, we offer promising future research opportunities. We believe that a deeper understanding of the behavior of existing methods can provide valuable new insights for future research.
Abstract:Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), where memory can enable knowledge accumulation, iterative reasoning and self-evolution. A number of memory methods have been proposed in the literature. However, these methods have not been systematically and comprehensively compared under the same experimental settings. In this paper, we first summarize a unified framework that covers existing representative agent memory methods from a high-level perspective. We then extensively compare representative agent memory methods on two long-term conversational benchmarks and an agentic memory benchmark, and examine the effectiveness of representative methods, providing a thorough analysis of those methods. As a byproduct of our experimental analysis, we also design a new memory method by exploiting modules in the existing methods, which outperforms the state-of-the-art methods. Finally, based on these findings, we offer promising future research opportunities. We believe that a deeper understanding of the behavior of existing methods can provide valuable new insights for future research.
Computation and Language (cs.CL); Databases (cs.DB)
arXiv:2604.01707 [cs.CL]
(or arXiv:2604.01707v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2604.01707 Focus to learn more arXiv-issued DOI via DataCite
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