智能体需要“睡眠”吗？基于 RRR 睡眠理论的 LLM 智能体记忆巩固分析
Do agents need to sleep?
AI 导读
一篇 arXiv 论文提出，持续运行、跨会话积累记忆的 LLM 智能体会出现语义漂移、检索精度下降、存储错误传播和记忆容量饱和等问题，这与生物睡眠所解决的问题相似。
正文 · 原文
Abstract:LLM-based agents, which increasingly operate persistently across sessions and accumulate memory through interaction, already exhibit the familiar symptoms of unchecked plasticity: semantic drift, degraded retrieval precision, propagation of stored errors, and saturation of memory capacity. Drawing on the selectionist "ringing, resonance and renormalization" (RRR) account of sleep, we propose that each of its three phases maps onto an operation feasible in digital memory architectures. Engineers building stateful agents, meanwhile, are independently rediscovering the problems sleep evolved to solve: what to forget, and when to do the forgetting. From this we derive the falsifiable prediction that selectionist consolidation will outperform instructionist alternatives as memory stores grow large. The core claim concerns the nature of continuous learning itself: plasticity has a price, and for any system that learns continuously under finite capacity while coupled to its environment, that price is sleep.
| Subjects: | Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.02216 [q-bio.NC] |
| (or arXiv:2611.02216v2 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2611.02216 arXiv-issued DOI via DataCite |
Submission history
From: Ido Haber [view email]
[v1]
Sun, 1 Nov 2026 21:24:59 UTC (235 KB)
[v2]
Tue, 3 Nov 2026 17:52:05 UTC (235 KB)
来源：arXiv cs.AI · arxiv.org