A BLOCKCHAIN PRIVACY-PRESERVING DEEP AGENTIC VALIDATION FRAMEWORK USING ZERO-KNOWLEDGE PROOFS

Abstract

Consensus protocols such as Proof-of-Work (PoW) and Proof-of-Stake (PoS) remain the backbone of most public blockchains, yet both impose steep energy costs, sizeable confirmation delays, and only partial transaction privacy. This paper presents the Deep Agentic Validation Framework (DAVF), a consensus-free alternative in which a population of Deep Reinforcement Learning Agents (DRLA) autonomously validates transactions and is coupled with zero-knowledge proofs (zk-SNARKs) to preserve confidentiality without sacrificing verifiability. Each agent learns a validation policy through Proximal Policy Optimization, trading off throughput, energy use, accuracy, and latency within a single reward signal, while zk-SNARK verification confirms transaction correctness without revealing transaction content. DAVF was implemented in a 50-node simulated peer-to-peer network and benchmarked on Ethereum transaction traces against PoW, PoS, and Practical Byzantine Fault Tolerance (PBFT). The framework attained a validation accuracy of 99.7% ± 0.2%, energy consumption of 0.012 kWh per transaction, a mean latency of 0.8 s, and a sustained throughput of 80 transactions per second, corresponding to roughly a 400-fold reduction in energy use relative to PoW and a 20% latency improvement over PBFT. Independent-samples t-tests and one-way ANOVA confirmed that these gains were statistically significant (p < 0.001) across all measured metrics. The results indicate that combining learning-based validation with succinct zero-knowledge cryptography is a practical route toward blockchain systems that are simultaneously energy-efficient, privacy-preserving, and scalable enough for real-world financial deployment.