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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2506.00430 |
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| _version_ | 1866917416478441472 |
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| author | Hsing, Nicole |
| author_facet | Hsing, Nicole |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00430 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MIRROR: Converging Cognitive Principles as Computational Mechanisms for AI Reasoning Hsing, Nicole Artificial Intelligence 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. |
| title | MIRROR: Converging Cognitive Principles as Computational Mechanisms for AI Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00430 |