A multi-agent framework for verifiable AIGC licensing in digital ecosystems

Abstract

The emergence of AI-generated content (AIGC) presents intricate difficulties concerning authorship, ownership, and validation of digital assets. These difficulties reveal a significant deficiency in existing governance frameworks, especially regarding the licensing and traceability of synthetic media. This study introduces a modular, multi-agent framework (MAG-AIGC) that utilizes Retrieval-Augmented Generation (RAG) and blockchain technologies to automate content registration, license interpretation, and provenance verification. The suggested architecture was formulated using the Design Science Research (DSR) approach and assessed over 30 relevant AIGC-licensing scenarios. Experimental findings indicate that the system attains up to 90% accuracy in licensing compliance detection, concurrently ensuring minimal latency and transaction costs. In addition to its technological contributions, the framework enhances trust, transparency, and accountability within the emerging AIGC ecosystem, providing actionable insights for regulators, creators, and platform developers to implement verifiable digital governance mechanisms and ensure reliability in AIGC-based commerce.

Keywords

• Artificial Intelligence
• AI-Generated Content (AIGC)
• Blockchain
• Multi-Agent Systems
• Automated Governance
• Digital Assets

Dijital ekosistemlerde doğrulanabilir AIGC lisanslaması için çoklu ajan çerçevesi

Abstract

Yapay zekâ tarafından üretilen içeriklerin (AIGC) ortaya çıkışı, dijital varlıkların yazarlığı, mülkiyeti ve doğrulanması açısından karmaşık sorunlar doğurmuştur. Bu sorunlar, özellikle sentetik medyanın lisanslanması ve izlenebilirliği konusunda mevcut yönetişim çerçevelerinde önemli bir yetersizliği açığa çıkarmaktadır. Bu çalışma, içerik kaydını, lisans yorumlamasını ve köken doğrulamasını otomatikleştirmek için bilgiye dayalı üretim (Retrieval-Augmented Generation, RAG) ve blok zinciri teknolojilerini kullanan modüler, çoklu aracılı bir çerçeve (MAG-AIGC) önermektedir. Önerilen mimari, Tasarım Bilimi Araştırması (Design Science Research, DSR) yaklaşımıyla geliştirilmiş ve 30’dan fazla AIGC lisanslama senaryosu üzerinde değerlendirilmiştir. Deneysel bulgular, sistemin lisans uyumluluğu tespitinde %90’a varan doğruluk oranına ulaştığını ve aynı anda düşük gecikme süresi ile işlem maliyeti sağladığını göstermektedir. Teknolojik katkılarının ötesinde, bu çerçeve gelişmekte olan AIGC ekosisteminde güveni, şeffaflığı ve hesap verebilirliği artırmakta, düzenleyiciler, içerik üreticileri ve platform geliştiricileri için doğrulanabilir dijital yönetişim mekanizmalarının uygulanması ve AIGC tabanlı ticarette güvenilirliğin sağlanması adına uygulanabilir içgörüler sunmaktadır.

Keywords

• yapay zeka
• yapay zeka tarafından üretilen içerik (aigc)
• blok zinciri
• çoklu ajan sistemleri
• dijital varlıklar

References

1. Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P., and Sun, L. (2025). A survey of AI-generated content (AIGC). ACM Computing Surveys, 57, 125. Doi: https://doi.org/10.1145/3704262
2. Chaffer, T. J. (2025). Governing the agent-to-agent economy of trust via progressive decentralization. arXiv, arXiv:2501.16606.
3. Chohan, U. W. (2021). Non-fungible tokens: Blockchains, scarcity, and value. Critical Blockchain Research Initiative (CBRI) Working Papers, 14. Doi: https://doi.org/10.2139/ssrn.3822743
4. Creative Commons. (2023). Understanding CC Licenses and Generative AI. Retrieved from https://creativecommons.org/2023/08/18/understanding-cc-licenses-and-generative-ai/
5. Cyberspace Administration of China. (2023). Interim Measures for the Management of Generative Artificial Intelligence Services. Beijing: CAC Press.
6. Daniel, E., and Tschorsch, F. (2022). IPFS and friends: A qualitative comparison of next generation peer-to-peer data networks. IEEE Communications Surveys & Tutorials, 24, 31–52. Doi: https://doi.org/10.1109/COMST.2022.3143147
7. Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L. (2023). QLORA: Efficient finetuning of quantized LLMs. In Proceedings of the 37th International Conference on Neural Information Processing Systems (NeurIPS) (pp. 10088–10115).
8. European Commission. (2024). Artificial Intelligence Act – Regulation (EU) 2024/1689. Official Journal of the European Union.

9. Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., and Chua, T.-S. (2024). A survey on RAG meeting LLMs: Towards retrieval-augmented large language models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 6491–6501).
10. Foo, L. G., Rahmani, H., and Liu, J. (2025). AI-Generated Content (AIGC) for Various Data Modalities: A Survey. ACM Computing Surveys, 57(9). Doi: https://doi.org/10.1145/3728633
11. Hammi, B., Zeadally, S., and Perez, A. J. (2023). Non-fungible tokens: A review. IEEE Internet of Things Magazine, 6, 46–50. Doi: https://doi.org/10.1109/IOTM.001.2200244
12. Hanneke, B., Heß, M., and Hinz, O. (2025). Foundations of decentralized metaverse economies: Converging physical and virtual realities. Journal of Management Information Systems, 42, 238–272. Doi: https://doi.org/10.1080/07421222.2025.2452017
13. Hevner, A. R., March, S. T., Park, J., and Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28, 75–105.
14. Huynh-The, T., Gadekallu, T. R., Wang, W., Yenduri, G., Ranaweera, P., Pham, Q.-V., ... Liyanage, M. (2023). Blockchain for the metaverse: A review. Future Generation Computer Systems, 143, 401–419. Doi: https://doi.org/10.1016/j.future.2023.02.008
15. Kaal, W. A. (2025). AI governance via Web3 reputation system. Stanford Journal of Blockchain Law & Policy.
16. Karandikar, N., Chakravorty, A., and Rong, C. (2021). Blockchain Based Transaction System With Fungible and Non-Fungible Tokens for a Community-Based Energy Infrastructure. Sensors, 21, 3822. Doi: https://doi.org/10.3390/s21113822
17. Karim, M. M., Van, D. H., Khan, S., Qu, Q., and Kholodov, Y. (2025). AI agents meet blockchain: A survey on secure and scalable collaboration for multi-agents. Future Internet, 17, 57. Doi: https://doi.org/10.3390/fi17020057
18. Khan, S. N., Loukil, F., Ghedira-Guegan, C., et al. (2021). Blockchain smart contracts: Applications, challenges, and future trends. Peer-to-Peer Networking and Applications, 14, 2901–2925.
19. Ko, H., Oh, J., and Kim, S. U. (2023). Digital content management using non-fungible tokens and the interplanetary file system. Applied Sciences, 14, 315. Doi: https://doi.org/10.3390/app14010315
20. Lin, Y., Du, H., Niyato, D., Nie, J., Zhang, J., Cheng, Y., and Yang, Z. (2023). Blockchain-Aided Secure Semantic Communication for AI-Generated Content in Metaverse. IEEE Open Journal of the Computer Society, 4, 72–83. Doi: https://doi.org/10.1109/OJCS.2023.3260732
21. Liu, Y., Du, H., Niyato, D., Kang, J., Xiong, Z., Miao, C., ... Jamalipour, A. (2024). Blockchain-Empowered Lifecycle Management for AI-Generated Content Products in Edge Networks. IEEE Wireless Communications, 31, 286–294. Doi: https://doi.org/10.1109/MWC.003.2300053
22. Luo, H., Luo, J., and Vasilakos, A. V. (2024). BC4LLM: A perspective of trusted artificial intelligence when blockchain meets large language models. Neurocomputing, 599, 128089. Doi: https://doi.org/10.1016/j.neucom.2024.128089
23. Mezzi, E., Mertzani, A., Manis, M. P., Lilova, S., Vadivoulis, N., Gatirdakis, S., ... Hmede, R. (2025). Who owns the output? Bridging law and technology in LLMs attribution. arXiv, arXiv:2504.01032.
24. Miao, J., Thongprayoon, C., Suppadungsuk, S., Garcia Valencia, O. A., & Cheungpasitporn, W. (2024). Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications. Medicina, 60(3), 445. Doi: https://doi.org/10.3390/medicina60030445
25. Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. SSRN Electronic Journal.
26. Ouyang, L., Yuan, Y., and Wang, F.-Y. (2022). Learning markets: An AI collaboration framework based on blockchain and smart contracts. IEEE Internet of Things Journal, 9, 14273–14286. Doi: https://doi.org/10.1109/JIOT.2020.3032706
27. Peffers, K., Tuunanen, T., Rothenberger, M. A., and Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24, 45–77. Doi: https://doi.org/10.2753/MIS0742-1222240302
28. Qwen Team. (2024). Qwen2 Technical Report. arXiv, arXiv:2407.10671.
29. Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. Doi: https://doi.org/10.1016/j.iotcps.2023.04.003
30. Salah, K., Rehman, M. H. U., Nizamuddin, N., and Al-Fuqaha, A. (2019). Blockchain for AI: Review and open research challenges. IEEE Access, 7, 10127–10149. Doi: https://doi.org/10.1109/ACCESS.2018.2890507
31. Singh, A., Ehtesham, A., Kumar, S., and Talaei Khoei, T. (2025). Agentic retrieval-augmented generation: A survey on Agentic RAG. arXiv, arXiv:2501.09136.
32. Tanriverdi, M. (2024). PublicEduChain: A Framework for Sharing Student-Owned Educational Data on Public Blockchain Network. IEEE Access, 12, 51772–51785. Doi: https://doi.org/10.1109/ACCESS.2024.3385660
33. Truong, V. T., Le, H. D., and Le, L. B. (2024). Trust-Free Blockchain Framework for AI-Generated Content Trading and Management in Metaverse. IEEE Access, 12, 41815–41828. Doi: https://doi.org/10.1109/ACCESS.2024.3376509
34. U.S. Department of Commerce, National Telecommunications and Information Administration (NTIA). (2024). AI Accountability Policy Report: Executive Overview. Washington, D.C.: U.S. Department of Commerce. Retrieved from https://www.ntia.gov/issues/artificial-intelligence/ai-accountability-policy-report/overview.
35. Wang, S., Ding, W., Li, J., Yuan, Y., Ouyang, L., and Wang, F.-Y. (2019). Decentralized autonomous organizations: Concept, model, and applications. IEEE Transactions on Computational Social Systems, 6, 870–878. Doi: https://doi.org/10.1109/TCSS.2019.2938190
36. Wang, Y., Pan, Y., Yan, M., Su, Z., and Luan, T. H. (2023). A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions. IEEE Open Journal of the Computer Society, 4, 280–302.
37. Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H., and Shen, X. (2023). A survey on metaverse: Fundamentals, security, and privacy. IEEE Communications Surveys & Tutorials, 25, 319–352.
38. Xu, J., Zhang, J., and Wang, J. (2025). Digital image copyright protection and management approach—Based on artificial intelligence and blockchain technology. Journal of Theoretical and Applied Electronic Commerce Research, 20, 76. Doi: https://doi.org/10.3390/jtaer20020076
39. Yang, F., Abedin, M. Z., Qiao, Y., and Ye, L. (2024). Toward Trustworthy Governance of AI-Generated Content (AIGC): A Blockchain-Driven Regulatory Framework for Secure Digital Ecosystems. IEEE Transactions on Engineering Management, 71, 14945–14962.
40. Zhang, Q., et al. (2025). Exploring Edge-Driven Collaborative Fine-Tuning toward Customized AIGC Services. IEEE Network, 39, 293–301.
41. Zhang, Q., Wu, Gr., Yang, R., et al. (2024). Digital image copyright protection method based on blockchain and zero trust mechanism. Multimedia Tools and Applications, 83, 77267–77302.