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.
[20] M. Kleppmann, A. Wiggins, P. van Hardenberg, and M. McGranaghan, “Local-First Software: You Own Your Data, in spite of the Cloud,” in Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Oct. 2019, pp. 154-178. doi: 10.1145/3359591.3359737. [Online]. Available: inkandswitch.com/essay/local-first
[21] S. Ango, “File over app,” July 01, 2023. Accessed: Aug. 18, 2026. [Online]. Available: stephango.com/file-over-app