Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework
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...
| Authors | Yanchen Wu, Tenghui Lin, Yingli Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 April 2026 |
| Last updated at source | 6 August 2026 |
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