DLG-Net: A Physics-Constrained Discrete Liquid Graph Network with Active Canary Traps for Anti-Hallucination

Abstract: To address systematic misclassification and adversarial tampering in extreme edge environments, this paper proposes a Physics-Constrained Discrete Liquid Graph (DLG) anti-hallucination security base. The architecture employs a deterministic physical gating to eliminate false positives, introduces heterogeneous cross-examination with Active Canary Traps to resist data poisoning, and integrates dynamic synaptic pruning with graceful degradation. Theoretically, by combining time-varying topology with discrete Lyapunov analysis, we strictly prove bounded convergence and asymptotic stability. Evaluations on the MassBank dataset demonstrate that the DLG architecture maintains an F1-Score above 0.99 under various adversarial attacks, restricts the false positive rate to 1.3%, and achieves a pure CPU inference latency of merely 5.31 ms. This work provides a novel paradigm for next-generation trustworthy edge AI.