To Know is to Construct: Schema-Constrained Generation for Agent Memory
This work addresses two key limitations in existing agent memory systems: dense retrieval often fails to distinguish semantically similar yet contextually distinct memory items, introducing noise, while open-ended generation is prone to structural hallucinations. Inspired by constructivist epistemology, this study introduces cognitive schemata into large language model memory mechanisms for the first time, proposing a schema-constrained generation approach that ensures only valid memory keys are produced. By integrating assimilation and accommodation processes, the method enables long-term memory evolution. Dynamic maintenance of cognitive schemata, coupled with activation propagation over an associative graph, supports multi-hop reasoning. Evaluated on the LoCoMo benchmark, the proposed approach significantly outperforms all retrieval baselines, achieving consistent performance gains across all categories.