A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering
Reasoning and question answering, as fundamental cognitive functions in humans, remain significant hurdles for artificial intelligence. While large language models (LLMs) have achieved notable success, integrating explicit memory with structured reasoning capabilities remains a persistent difficulty...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1635932/full |
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| author | Yao Liang Yao Liang Yuwei Wang Yuwei Wang Hongjian Fang Hongjian Fang Feifei Zhao Yi Zeng Yi Zeng Yi Zeng Yi Zeng Yi Zeng |
| author_facet | Yao Liang Yao Liang Yuwei Wang Yuwei Wang Hongjian Fang Hongjian Fang Feifei Zhao Yi Zeng Yi Zeng Yi Zeng Yi Zeng Yi Zeng |
| author_sort | Yao Liang |
| collection | DOAJ |
| description | Reasoning and question answering, as fundamental cognitive functions in humans, remain significant hurdles for artificial intelligence. While large language models (LLMs) have achieved notable success, integrating explicit memory with structured reasoning capabilities remains a persistent difficulty. The Differentiable Neural Computer (DNC) model, despite addressing these issues to some extent, still faces challenges such as algorithmic complexity, slow convergence, and limited robustness. Inspired by the brain's learning and memory mechanisms, this paper proposes a Memory Transformation based Differentiable Neural Computer (MT-DNC) model. The MT-DNC integrates two brain-inspired memory modules—a working memory module inspired by the cognitive system that temporarily holds and processes task-relevant information, and a long-term memory module that stores frequently accessed and enduring information—within the DNC framework, enabling the autonomous transformation of acquired experiences between these memory systems. This facilitates efficient knowledge extraction and enhances reasoning capabilities. Experimental results on the bAbI question answering task demonstrate that the proposed method outperforms existing Deep Neural Network (DNN) and DNC models, achieving faster convergence and superior performance. Ablation studies further confirm that the transformation of memory from working memory to long-term memory is critical for improving the robustness and stability of reasoning. This work offers new insights into incorporating brain-inspired memory mechanisms into dialogue and reasoning systems. |
| format | Article |
| id | doaj-art-e4f148d9948a404d9bbd8e9e0ee46e70 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-e4f148d9948a404d9bbd8e9e0ee46e702025-08-20T03:02:52ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-08-01810.3389/frai.2025.16359321635932A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answeringYao Liang0Yao Liang1Yuwei Wang2Yuwei Wang3Hongjian Fang4Hongjian Fang5Feifei Zhao6Yi Zeng7Yi Zeng8Yi Zeng9Yi Zeng10Yi Zeng11Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaBrain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaCenter for Long-term Artificial Intelligence, Beijing, ChinaBrain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaSchool of Future Technology, University of Chinese Academy of Sciences, Beijing, ChinaBrain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaBrain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaCenter for Long-term Artificial Intelligence, Beijing, ChinaSchool of Future Technology, University of Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences, Shanghai, ChinaReasoning and question answering, as fundamental cognitive functions in humans, remain significant hurdles for artificial intelligence. While large language models (LLMs) have achieved notable success, integrating explicit memory with structured reasoning capabilities remains a persistent difficulty. The Differentiable Neural Computer (DNC) model, despite addressing these issues to some extent, still faces challenges such as algorithmic complexity, slow convergence, and limited robustness. Inspired by the brain's learning and memory mechanisms, this paper proposes a Memory Transformation based Differentiable Neural Computer (MT-DNC) model. The MT-DNC integrates two brain-inspired memory modules—a working memory module inspired by the cognitive system that temporarily holds and processes task-relevant information, and a long-term memory module that stores frequently accessed and enduring information—within the DNC framework, enabling the autonomous transformation of acquired experiences between these memory systems. This facilitates efficient knowledge extraction and enhances reasoning capabilities. Experimental results on the bAbI question answering task demonstrate that the proposed method outperforms existing Deep Neural Network (DNN) and DNC models, achieving faster convergence and superior performance. Ablation studies further confirm that the transformation of memory from working memory to long-term memory is critical for improving the robustness and stability of reasoning. This work offers new insights into incorporating brain-inspired memory mechanisms into dialogue and reasoning systems.https://www.frontiersin.org/articles/10.3389/frai.2025.1635932/fullneural turing machinememory-augmented networksreasoning and question answeringworking/long-term memorydifferentiable neural computer |
| spellingShingle | Yao Liang Yao Liang Yuwei Wang Yuwei Wang Hongjian Fang Hongjian Fang Feifei Zhao Yi Zeng Yi Zeng Yi Zeng Yi Zeng Yi Zeng A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering Frontiers in Artificial Intelligence neural turing machine memory-augmented networks reasoning and question answering working/long-term memory differentiable neural computer |
| title | A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering |
| title_full | A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering |
| title_fullStr | A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering |
| title_full_unstemmed | A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering |
| title_short | A brain-inspired memory transformation based differentiable neural computer for reasoning-based question answering |
| title_sort | brain inspired memory transformation based differentiable neural computer for reasoning based question answering |
| topic | neural turing machine memory-augmented networks reasoning and question answering working/long-term memory differentiable neural computer |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1635932/full |
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