References

References#

The works cited throughout this book are listed below.

[1]

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Arthur L. Samuel. Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3):210–229, 1959. doi:10.1147/rd.33.0210.

[3]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436–444, 2015. doi:10.1038/nature14539.

[4]

Rishi Bommasani, Drew A Hudson, and others. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.

[5]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30, 5998–6008. 2017.

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Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, and others. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, volume 35, 27730–27744. 2022. arXiv:2203.02155.

[7]

Devharsh Trivedi. When the tool agrees with you: large language model sycophancy, examiner impartiality, and the admissibility of AI-assisted forensic opinion. Journal of Cybersecurity, Digital Forensics and Jurisprudence, 2:85–99, 2026. doi:10.65879/3070-5789.2026.02.06.

[8]

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, volume 35, 24824–24837. 2022.

[9]

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, and others. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature, 645(8081):633–638, 2025. arXiv:2501.12948. doi:10.1038/s41586-025-09422-z.

[10]

Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, and Shane Legg. Position: levels of AGI for operationalizing progress on the path to AGI. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learning Research, 36308–36321. 2024. arXiv:2311.02462.

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Dan Hendrycks, Dawn Song, Christian Szegedy, and others. A definition of AGI. https://arxiv.org/abs/2510.18212, 2025. 33 authors, including Yoshua Bengio. Accessed 2026-10-16. arXiv:2510.18212.

[12]

Nick Bostrom. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014.

[13]

Devharsh Trivedi. Verifiable constraints, not self-attestation: bounding AI in security systems. Presentation, 2026. doi:10.13140/RG.2.2.20659.18726.

[14]

Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning. In International Conference on Learning Representations. 2025. arXiv:2408.03314.

[15]

Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. S1: simple test-time scaling. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 20275–20321. Association for Computational Linguistics, 2025. doi:10.18653/v1/2025.emnlp-main.1025.

[16]

Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. Do NOT think that much for 2+3=? on the overthinking of long reasoning models. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, 9487–9499. 2025. arXiv:2412.21187.

[17]

Aryo Pradipta Gema, Alexander Hägele, Runjin Chen, Andy Arditi, Jacob Goldman-Wetzler, Kit Fraser-Taliente, Henry Sleight, Linda Petrini, Julian Michael, Beatrice Alex, Pasquale Minervini, Yanda Chen, Joe Benton, and Ethan Perez. Inverse scaling in test-time compute. Transactions on Machine Learning Research, 2025. arXiv:2507.14417.

[18]

Devharsh Trivedi. ProofLayer: control coverage screening for government cybersecurity policies via multi-LLM orchestration. Journal of Cybersecurity, Digital Forensics and Jurisprudence, 2:100–111, 2026. doi:10.65879/3070-5789.2026.02.07.

[19]

Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems, volume 35. 2022.

[20]

Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. International Conference on Learning Representations (ICLR), 2023.

[21]

Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 2023.

[22]

Devharsh Trivedi, Nesrine Kaaniche, Nikos Triandopoulos, Maryline Laurent, and Jackson Walters. Fairis: fairness-aware aggregation with provable influence containment against fairness poisoning attacks in collaborative machine learning. arXiv preprint, 2026. arXiv:2608.06469, doi:10.48550/arXiv.2608.06469.

[23]

Devharsh Trivedi. Engineering secure AI systems with lattice-based cryptography and large language models. Engineering Science & Technology, pages 129–187, 2026. doi:10.37256/est.81202710357.