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.

💡 Research Summary

The paper introduces DMind‑3, a sovereign Edge‑Local‑Cloud intelligence stack designed to protect irreversible Web3 financial transactions against adversarial threats while meeting strict latency requirements. Existing cloud‑centric assistants offer rich global context but suffer from unpredictable latency and privacy leakage; purely local solutions preserve privacy and speed but lack ecosystem awareness. DMind‑3 resolves this tension by decomposing functionality into three cooperating layers. The Edge tier runs a lightweight component inside the browser or wallet that parses raw calldata, detects high‑risk patterns (e.g., unlimited approvals, delegate misuse), and enforces deterministic signing‑time policies that operate independently of network conditions. The Local tier resides on user‑controlled hardware, keeping private portfolio data on‑device while employing a compressed high‑fidelity reasoning engine to interpret contracts, analyze economic exploits, and simulate strategies. The Cloud tier aggregates macro‑level signals from the entire blockchain ecosystem and synthesizes them using a novel Hierarchical Predictive Synthesis (HPS) objective that captures time‑varying correlations.

Policy‑driven selective offloading classifies each transaction request by privacy sensitivity, latency criticality, and uncertainty, routing sub‑problems to the appropriate tier. When uncertainty rises, a risk‑aware orchestration plane triggers a Contrastive Chain‑of‑Correction Supervised Fine‑Tuning (C³‑SFT) mechanism that cross‑validates local reasoning against cloud‑generated hypotheses, emphasizing correction‑oriented learning to improve verification reliability. The final decision is bound to the transaction bytes at the Edge via a deterministic policy gate, ensuring that no external service can alter the signed output.

Extensive evaluations demonstrate that DMind‑3 maintains average signing latency below 120 ms even under network congestion, reduces privacy leakage risk by roughly 85 % compared to cloud‑only baselines, and achieves a 93.7 % multi‑turn success rate on protocol‑constrained tasks. Domain‑specific reasoning accuracy surpasses general‑purpose LLM baselines by over 18 %. The authors contribute (1) the Edge‑Local‑Cloud architecture with explicit trust boundaries, (2) policy‑driven selective offloading and risk‑aware orchestration, (3) the HPS and C³‑SFT training objectives, and (4) a comprehensive evaluation methodology covering latency, privacy, and robustness. The work demonstrates that anchoring safety at the edge while leveraging global context in a controlled manner provides a scalable, sovereign solution for time‑critical, irreversible financial decision making in Web3 and potentially other adversarial domains.