Wenlu 系统融合大模型与领域知识，实现安全多模态决策与硬件代码自动生成。
A "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Secure New Architecture for Deep Integration of Foundation Models and Domain Knowledge
- 受脑科学启发，用记忆标记与重放机制融合私有数据与通用模型。
- 支持图像、语音等多模态输入，可端到端生成硬件控制代码。
- 适合企业决策、医疗分析、自动驾驶等需安全与自主的场景。
随着人工智能在各行业快速渗透，构建下一代智能核心的关键挑战在于如何有效整合基础模型的语言理解能力与复杂现实应用中的领域知识。本文提出一种名为「Wenlu」的多模态认知与具身决策脑系统，旨在实现私有知识与公共模型的安全融合，统一处理图像、语音等多模态数据，并从认知到自动产生硬件级代码完成闭环决策。系统引入类脑记忆标记与重放机制，无缝集成用户私有数据、行业知识与通用语言模型。该系统为企业决策支持、医学分析、自动驾驶、机器人控制等提供精准高效的多模态服务。相比现有方案，Wenlu 在多模态处理、隐私安全、端到端硬件控制代码生成、自学习与可持续更新方面具有显著优势，为构建下一代智能核心奠定坚实基础。
原文摘要 · Abstract (English)
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.
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