A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912406178889728 |
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| author | Geng, Liang |
| author_facet | Geng, Liang |
| contents | With the rapid penetration of artificial intelligence across industries and scenarios, a key challenge in building the next-generation intelligent core lies in effectively integrating the language understanding capabilities of foundation models with domain-specific knowledge bases in complex real-world applications. This paper proposes a multimodal cognition and embodied decision-making brain system, ``Wenlu", designed to enable secure fusion of private knowledge and public models, unified processing of multimodal data such as images and speech, and closed-loop decision-making from cognition to automatic generation of hardware-level code. The system introduces a brain-inspired memory tagging and replay mechanism, seamlessly integrating user-private data, industry-specific knowledge, and general-purpose language models. It provides precise and efficient multimodal services for enterprise decision support, medical analysis, autonomous driving, robotic control, and more. Compared with existing solutions, ``Wenlu" demonstrates significant advantages in multimodal processing, privacy security, end-to-end hardware control code generation, self-learning, and sustainable updates, thus laying a solid foundation for constructing the next-generation intelligent core. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00570 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge Geng, Liang Artificial Intelligence With the rapid penetration of artificial intelligence across industries and scenarios, a key challenge in building the next-generation intelligent core lies in effectively integrating the language understanding capabilities of foundation models with domain-specific knowledge bases in complex real-world applications. This paper proposes a multimodal cognition and embodied decision-making brain system, ``Wenlu", designed to enable secure fusion of private knowledge and public models, unified processing of multimodal data such as images and speech, and closed-loop decision-making from cognition to automatic generation of hardware-level code. The system introduces a brain-inspired memory tagging and replay mechanism, seamlessly integrating user-private data, industry-specific knowledge, and general-purpose language models. It provides precise and efficient multimodal services for enterprise decision support, medical analysis, autonomous driving, robotic control, and more. Compared with existing solutions, ``Wenlu" demonstrates significant advantages in multimodal processing, privacy security, end-to-end hardware control code generation, self-learning, and sustainable updates, thus laying a solid foundation for constructing the next-generation intelligent core. |
| title | A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00570 |