类人终身记忆：一种基于神经科学的架构，用于无限交互
Human-Like Lifelong Memory: A Neuroscience-Grounded Architecture for Infinite Interaction
AI总结：
本文提出一种基于神经科学的类人终身记忆架构，通过结合互补学习系统理论、认知行为疗法的信念层级、双进程认知和模糊轨迹理论，解决大语言模型在长期交互和情境敏感检索中的记忆不足问题。
摘要：
大型语言模型缺乏持久且结构化的长期交互和情境敏感检索记忆。扩展上下文窗口并不能解决这一问题：近期证据表明，仅增加上下文长度会降低推理能力高达85% - 即使检索完美。我们提出了一种生物启发式的记忆框架，基于互补学习系统理论、认知行为疗法的信念层级、双进程认知和模糊轨迹理论，围绕三个原则：（1）记忆具有情感价值而非仅仅是内容 - 预计算的情感关联摘要（情感向量）在由贝克认知模型启发的涌现信念层级中组织，使在深入思考前能够立即定向；（2）检索默认使用系统1，系统2升级 - 自动扩散激活和被动提示作为默认，只有在需要时才进行主动检索，具有分级认知状态以结构化地解决幻觉问题；（3）编码是主动的、当前的、反馈依赖的 - 丘脑网关标记并路由信息到存储库，而执行系统通过好奇心驱动的探索形成概要，而不是被动暴露。七个功能特性定义了任何实现必须满足的条件。随着时间的推移，系统会朝着系统1处理 - 临床专长的计算类比 - 收敛，产生随着经验积累而变得更经济而非更昂贵的交互。
英文摘要：
Large language models lack persistent, structured memory for long-term interaction and context-sensitive retrieval. Expanding context windows does not solve this: recent evidence shows that context length alone degrades reasoning by up to 85% - even with perfect retrieval. We propose a bio-inspired memory framework grounded in complementary learning systems theory, cognitive behavioral therapy's belief hierarchy, dual-process cognition, and fuzzy-trace theory, organized around three principles: (1) Memory has valence, not just content - pre-computed emotional-associative summaries (valence vectors) organized in an emergent belief hierarchy inspired by Beck's cognitive model enable instant orientation before deliberation; (2) Retrieval defaults to System 1 with System 2 escalation - automatic spreading activation and passive priming as default, with deliberate retrieval only when needed, and graded epistemic states that address hallucination structurally; and (3) Encoding is active, present, and feedback-dependent - a thalamic gateway tags and routes information between stores, while the executive forms gists through curiosity-driven investigation, not passive exposure. Seven functional properties specify what any implementation must satisfy. Over time, the system converges toward System 1 processing - the computational analog of clinical expertise - producing interactions that become cheaper, not more expensive, with experience.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
