将人类认知原理转化为AI推理架构，显著提升多轮对话表现。

Multiple cognitive theories -- Global Workspace Theory, reconstructive episodic memory, inner speech, and complementary learning systems -- converge on a shared set of architectural principles: parallel specialized processing, integrative synthesis into a bounded unified representation, and reconstructive rather than accumulative maintenance. We test whether these converging principles provide computational advantages when implemented in AI systems. MIRROR operationalizes each principle as a concrete mechanism: an Inner Monologue Manager generates parallel cognitive threads (Goals, Reasoning, Memory), a Cognitive Controller synthesizes these into a bounded first-person narrative that is fully reconstructed each turn, and a temporal separation between fast response generation and slow deliberative consolidation mirrors complementary learning dynamics. Evaluated on multi-turn dialogue requiring constraint maintenance under attentional interference, MIRROR yields 21% relative improvement across seven architecturally diverse language models. Ablation studies test the theoretical predictions directly: reconstructive synthesis improves all seven models (+5-20%); the integrated system outperforms either component alone for six of seven models, confirming that parallel exploration and integrative synthesis are complementary; and gains concentrate where theories predict -- under high attentional load where global availability of integrated information is most needed. These results demonstrate that converging principles from human cognition provide architecture-general computational advantages, and generate testable behavioral predictions about working memory, inner speech, and memory consolidation. Project page available at https://www.arcarae.com/research/MIRROR and code at https://github.com/arcarae/MIRROR.