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Citations

A selection of academic papers and resources that have influenced the development of ACORN

[1] E. Njor, M. A. Hasanpour, J. Madsen, and X. Fafoutis, “A Holistic Review of the TinyML Stack for Predictive Maintenance,” IEEE Access, vol. 12, pp. 184861-184882, 2024, doi: 10.1109/ACCESS.2024.3512860.

[2] Y. Yang et al., “A Survey of AI Agent Protocols,” Apr. 26, 2025, arXiv: arXiv:2504.16736. doi: 10.48550/arXiv.2504.16736.

[3] B. Liu et al., “Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems,” Mar. 31, 2025, arXiv: arXiv:2504.01990. doi: 10.48550/arXiv.2504.01990.

[4] “AI Blindspot: A Discovery Process for preventing, detecting, and mitigating bias in AI systems.” Accessed: Jan. 24, 2023. [Online]. Available: https://aiblindspot.media.mit.edu/

[5] V. Gadepally et al., “AI Enabling Technologies: A Survey,” May 08, 2019, arXiv: arXiv:1905.03592. doi: 10.48550/arXiv.1905.03592.

[6] A. Jain, S. Sharma, and S. Duggal, “Comparative Study of Various Process Model in Software Development,” 2013. Accessed: Jan. 24, 2023. [Online]. Available: semanticscholar.org

[7] Q. Hua et al., “Context Engineering 2.0: The Context of Context Engineering,” Oct. 30, 2025, arXiv: arXiv:2510.26493. doi: 10.48550/arXiv.2510.26493.

[8] N. D. Lawrence, “Data Readiness Levels,” May 05, 2017, arXiv: arXiv:1705.02245. doi: 10.48550/arXiv.1705.02245.

[9] A. Fuller, Z. Fan, C. Day, and C. Barlow, “Digital Twin: Enabling Technologies, Challenges and Open Research,” IEEE Access, vol. 8, pp. 108952-108971, 2020, doi: 10.1109/ACCESS.2020.2998358.

[10] J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge Distillation: A Survey,” Int J Comput Vis, vol. 129, no. 6, pp. 1789-1819, June 2021, doi: 10.1007/s11263-021-01453-z.

[11] D. Kreuzberger, N. Kühl, and S. Hirschl, “Machine Learning Operations (MLOps): Overview, Definition, and Architecture,” May 14, 2022, arXiv: arXiv:2205.02302. doi: 10.48550/arXiv.2205.02302.

[12] M. Mitchell et al., “Model Cards for Model Reporting,” in Proceedings of the Conference on Fairness, Accountability, and Transparency, Jan. 2019, pp. 220-229. doi: 10.1145/3287560.3287596.

[13] E. Blasch, J. Sung, and T. Nguyen, “Multisource AI Scorecard Table for System Evaluation,” Feb. 07, 2021, arXiv: arXiv:2102.03985. doi: 10.48550/arXiv.2102.03985.

[14] F. Yu, H. Zhang, and B. Wang, “Natural Language Reasoning, A Survey,” Mar. 26, 2023, arXiv: arXiv:2303.14725. doi: 10.48550/arXiv.2303.14725.

[15] S. Zhao, Y. Yang, Z. Wang, Z. He, L. K. Qiu, and L. Qiu, “Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely,” Sept. 23, 2024, arXiv: arXiv:2409.14924. Accessed: Oct. 02, 2024. [Online]. Available: arxiv.org

[16] Y. K. Liu, S. K. Ong, and A. Y. C. Nee, “State-of-the-art survey on digital twin implementations,” Adv. Manuf., vol. 10, no. 1, pp. 1-23, Mar. 2022, doi: 10.1007/s40436-021-00375-w.

[17] Center for Security and Emerging Technology and B. Buchanan, “The AI Triad and What It Means for National Security Strategy,” Center for Security and Emerging Technology, Aug. 2020. doi: 10.51593/20200021.

[18] J. M. Bradshaw, R. R. Hoffman, D. D. Woods, and M. Johnson, “The Seven Deadly Myths of ‘Autonomous Systems,’” IEEE Intelligent Systems, vol. 28, no. 3, pp. 54-61, May 2013, doi: 10.1109/MIS.2013.70.

[19] M. R. Endsley, “Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors Journal 37(1), 32-64,” ResearchGate, Aug. 2025, doi: 10.1518/001872095779049543.