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

Computer Science > Cryptography and Security [Submitted on 2 Aug 2026] Neuro-Symbolic Participation Governance for Verifiable AI Agents in Open Digital Twin Ecosystems Juan Li, Wei Cai, Yan Bai 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. Comments: Accepted at the 2026 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2026), Bellevue, WA, USA, October 4-7, 2026. 7 pages, 1 figure, 5 tables. Code: this https URL (DOI: https://doi.org/10.5281/zenodo.21706699) Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) ACM classes: I.2.11; K.6.5 Cite as: arXiv:2608.00937 [cs.CR]   (or arXiv:2608.00937v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.00937 Focus to learn more Submission history From: Wei Cai [view email] [v1] Sun, 2 Aug 2026 02:30:32 UTC (1,262 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)