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.

翻译：记忆系统是AI智能体的基础，然而现有工作往往缺乏对多样化任务的适应性，并忽视了AI智能体记忆的建设性与任务导向性。借鉴皮亚杰的认知发展理论，我们提出PISA——一种实用的心理启发统一记忆系统，通过将记忆视为建设性与适应性过程来解决上述局限。为实现持续学习与适应性，PISA引入三模态适应机制（即：图式更新、图式演化与图式创建），在保持连贯组织的同时支持灵活的记忆更新。基于这些图式化结构，我们进一步设计了一种混合记忆访问架构，将符号推理与神经检索无缝集成，显著提升了检索准确率与效率。我们在现有LOCOMO基准以及为数据分析任务新提出的AggQA基准上进行的实证评估表明，PISA通过显著增强适应性与长期知识保持能力，确立了新的最优性能水平。