Neuro-Symbolic Participation Governance for Verifiable AI Agents in Open Digital Twin Ecosystems

2026-08-01

AI Summary

This work addresses the lack of reliable mechanisms for verifying identity, capability, and policy compliance in AI multi-agent systems operating within cross-organizational, decentralized digital twin environments. To bridge this gap, the authors propose a neurosymbolic decentralized governance framework that uniquely integrates neurosymbolic reasoning with blockchain smart contracts. By leveraging formal domain ontologies and multi-layer semantic profiles, the framework establishes a machine-interpretable, policy-aware, and context-sensitive trustworthy collaboration mechanism. It enables verifiable and auditable interactions under strong privacy guarantees, effectively preventing unauthorized actions and enforcing institutional policies in a prototype implementation. Crucially, the approach maintains verification overhead within practical bounds, thereby demonstrating the feasibility of secure, cross-institutional human-AI collaboration.

Abstract

Autonomous AI agents, increasingly empowered by large language models, are becoming important components of human-machine systems for high-stakes decision support in digital twin ecosystems. However, existing multi-agent systems often lack robust verification for identity, capability, and policy compliance, especially in decentralized environments spanning multiple institutions. This paper proposes a neuro-symbolic decentralized governance framework for verifiable agents in collaborative digital twin environments. By representing agents through multi-layer semantic profiles, the framework bridges probabilistic neural reasoning with deterministic institutional governance, thereby supporting trustworthy human-AI collaboration and meaningful human oversight. Capabilities are grounded in formal domain ontologies to enable machine-interpretable, policy-aware, and context-sensitive participation. These credentials, issued by organizational authorities, are validated via blockchain-based smart contracts, ensuring auditable participation without exposing sensitive data. We demonstrate the framework using a decision-support prototype with clinic, digital twin, and wearable provider agents effectively prevents unauthorized interaction and enforces institutional policies with manageable overhead. Our findings suggest that neuro-symbolic decentralized governance provides a scalable and trustworthy pathway for safe human-machine collaboration across institutional boundaries.

Problem

• Verifiable AI Agents
• Digital Twin Ecosystems
• Decentralized Governance
• Policy Compliance
• Neuro-Symbolic

Innovation

• Neuro-Symbolic AI
• Decentralized Governance
• Verifiable Agents
• Digital Twin Ecosystems
• Blockchain-based Credentials