DMind-3: A Sovereign Edge--Local--Cloud AI System with Controlled Deliberation and Correction-Based Tuning for Safe, Low-Latency Transaction Execution

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.

DMind Benchmark: The First Comprehensive Benchmark for LLM Evaluation in the Web3 Domain

Recent advances in Large Language Models (LLMs) have led to significant progress on a wide range of natural language processing tasks. However, their effectiveness in specialized and rapidly evolving domains such as Web3 remains underexplored. In this paper, we introduce DMind Benchmark, a novel framework that systematically tests LLMs across nine key categories encompassing blockchain fundamentals, infrastructure, smart contract analysis, decentralized finance (DeFi), decentralized autonomous organizations (DAOs), non-fungible tokens (NFTs), token economics, meme concepts, and security vulnerabilities. DMind Benchmark goes beyond conventional multiple-choice questions by incorporating domain-specific subjective tasks (e.g., smart contract code auditing and repair, numeric reasoning on on-chain data, and fill-in assessments), thereby capturing real-world complexities and stress-testing model adaptability. We evaluate fifteen popular LLMs (from ChatGPT, DeepSeek, Claude, and Gemini series) on DMind Benchmark, uncovering performance gaps in Web3-specific reasoning and application, particularly in emerging areas like token economics and meme concepts. Even the strongest models face significant challenges in identifying subtle security vulnerabilities and analyzing complex DeFi mechanisms. To foster progress in this area, we publicly release our benchmark dataset, evaluation pipeline, and annotated results at http://www.dmind.ai, offering a valuable resource for advancing specialized domain adaptation and the development of more robust Web3-enabled LLMs.