受皮亚杰认知理论启发，构建可自适应演化的统一记忆系统提升AI长期学习能力
PISA: A Pragmatic Psych-Inspired Unified Memory System for Enhanced AI Agency
- 基于认知发展理论设计三模态记忆更新机制，支持动态演化与灵活组织
- 在LOCOMO和AggQA基准上实现最新性能，显著提升长时记忆保持与任务适应性
- 适合需要持续学习与复杂推理的智能体系统，如数据分析与自主决策场景
记忆系统是智能体的核心，但现有方法往往缺乏对多样化任务的适应性，且忽视了记忆的建构性与任务导向性。受皮亚杰认知发展理论启发，本文提出PISA——一种实用的、心理启发的统一记忆系统，将记忆视为建构与自适应过程。为实现持续学习与灵活性，PISA引入三模态适应机制（即模式更新、模式演化与模式创建），在保持结构连贯性的同时支持灵活记忆更新。在此基础上，设计混合记忆访问架构，融合符号推理与神经检索，显著提升检索准确率与效率。在现有LOCOMO基准及新提出的用于数据分析任务的AggQA基准上的实证评估表明，PISA在适应性与长期知识保留方面均达到新最优水平。
原文摘要 · Abstract (English)
Memory systems are fundamental to AI agents, yet existing work often lacks adaptability to diverse tasks and overlooks the constructive and task-oriented role of AI agent memory. Drawing from Piaget's theory of cognitive development, we propose PISA, a pragmatic, psych-inspired unified memory system that addresses these limitations by treating memory as a constructive and adaptive process. To enable continuous learning and adaptability, PISA introduces a trimodal adaptation mechanism (i.e., schema updation, schema evolution, and schema creation) that preserves coherent organization while supporting flexible memory updates. Building on these schema-grounded structures, we further design a hybrid memory access architecture that seamlessly integrates symbolic reasoning with neural retrieval, significantly improving retrieval accuracy and efficiency. Our empirical evaluation, conducted on the existing LOCOMO benchmark and our newly proposed AggQA benchmark for data analysis tasks, confirms that PISA sets a new state-of-the-art by significantly enhancing adaptability and long-term knowledge retention.
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