NeuroGraph — Adaptive Neural Gossip Protocol (ANGP) v4.3.1 Protocol Overview and Architecture

作者：NeuroGraph Research · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.22070399 · 研究领域：Blockchain Technology Applications and Security、Opportunistic and Delay-Tolerant Networks、Advanced Graph Neural Networks

NeuroGraph ANGP (Adaptive Neural Gossip Protocol) v4.3.1 is an emergent neural consensus protocol that achieves distributed agreement through local Hebbian computation rather than traditional BFT voting, Proof of Work, or Proof of Stake. The system introduces a nine-layer architecture—Transaction, Prediction, Consensus, Reputation, Security, Network, Sharding, Slot, and Finality—where an Adaptive Directed Acyclic Graph with Hebbian learn ing serves as the cognitive engine of each node. Consensus emerges as the reputation-weighted median of all node predictions, achieving O(nlogn) complexity per shard with a 20:1 learning rate asymmetry that reinforces honest convergence versus attacker divergence. The protocol employs three DAGs and four auxiliary graphs as component data structures: a Transaction DAG (append-only, per-shard), a Hebbian Prediction DAG (private per node), and a Batch Finality chain (linear, fork-free), overlaid by reputation, topology, shard communication, andcluster detection graphs. The system is validated against 37 distinct attacker types (T0–T36) across 2664 nodes (333 shards × 8 nodes/shard), demonstrating complete elimination of coordinated attacks at 90% adversarial fraction with zero false positives. Stress tests process 13.32 billion transactions across 20,000 steps with 100.05% finalization rate, reaching 50M+ aggregate TPS (simnet)