This paper introduces DMind-3, a sovereign Edge-Local-Cloud intelligence stack designed to secure irreversible financial execution in Web3 environments against adversarial risks and strict latency constraints. While existing cloud-centric assistants compromise privacy and fail under network congestion, and purely local solutions lack global ecosystem context, DMind-3 resolves these tensions by decomposing capability into three cooperating layers: a deterministic signing-time intent firewall at the edge, a private high-fidelity reasoning engine on user hardware, and a policy-governed global context synthesizer in the cloud. We propose policy-driven selective offloading to route computation based on privacy sensitivity and uncertainty, supported by two novel training objectives: Hierarchical Predictive Synthesis (HPS) for fusing time-varying macro signals, and Contrastive Chain-of-Correction Supervised Fine-Tuning (C$^3$-SFT) to enhance local verification reliability. Extensive evaluations demonstrate that DMind-3 achieves a 93.7% multi-turn success rate in protocol-constrained tasks and superior domain reasoning compared to general-purpose baselines, providing a scalable framework where safety is bound to the edge execution primitive while maintaining sovereignty over sensitive user intent.

翻译：本文介绍了DMind-3，一个主权边缘-本地-云智能栈，旨在保障Web3环境中不可逆金融交易执行的安全，以应对对抗性风险和严格的延迟约束。现有的以云为中心的助手会损害隐私，并在网络拥塞时失效，而纯本地解决方案则缺乏全球生态系统上下文。DMind-3通过将能力分解为三个协同层来解决这些矛盾：位于边缘的确定性签名时意图防火墙、运行在用户硬件上的私有高保真推理引擎，以及位于云端的策略治理的全局上下文合成器。我们提出了策略驱动的选择性卸载，根据隐私敏感性和不确定性来路由计算，并得到两个新颖训练目标的支持：用于融合时变宏观信号的分层预测合成，以及用于增强本地验证可靠性的对比校正链监督微调。广泛的评估表明，在协议约束任务中，DMind-3实现了93.7%的多轮成功率，并且在领域推理方面优于通用基线，提供了一个可扩展的框架，其中安全性被绑定到边缘执行原语，同时保持对敏感用户意图的主权。