ElemedexA pocket encyclopedia of AI architectures
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Sources

The site’s complete bibliography, in APA 7 style. The four core books, from which many of the technical and philosophical explanations are drawn, come first. In-text citations link to this page.

Core books

  • Alammar, J., & Grootendorst, M. (2024). Hands-on large language models: Language understanding and generation. O’Reilly Media.
  • Chollet, F. (2021). Deep learning with Python (2nd ed.). Manning Publications.
  • Buckner, C. J. (2024). From deep learning to rational machines: What the history of philosophy can teach us about the future of artificial intelligence. Oxford University Press. https://doi.org/10.1093/oso/9780197653302.001.0001
  • Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data mining: Practical machine learning tools and techniques (4th ed.). Morgan Kaufmann. https://doi.org/10.1016/C2015-0-02071-8

All references

534 references

A

  1. Abiri, G. (2025). Public constitutional AI. Georgia Law Review, 59(2), Article 5. https://digitalcommons.law.uga.edu/glr/vol59/iss2/5
  2. Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., Bodenstein, S. W., Evans, D. A., Hung, C.-C., O’Neill, M., Reiman, D., Tunyasuvunakool, K., Wu, Z., Žemgulytė, A., Arvaniti, E., . . . Jumper, J. M. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016), 493–500. https://doi.org/10.1038/s41586-024-07487-w
  3. Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9(1), 147–169. https://doi.org/10.1207/s15516709cog0901_7
  4. Adams, F., & Aizawa, K. (2001). The bounds of cognition. Philosophical Psychology, 14(1), 43–64. https://doi.org/10.1080/09515080120033571
  5. Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018). MesoNet: A compact facial video forgery detection network. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS) (pp. 1–7). IEEE. https://doi.org/10.1109/WIFS.2018.8630761
  6. Ainslie, J., Lee-Thorp, J., de Jong, M., Zemlyanskiy, Y., Lebrón, F., & Sanghai, S. (2023). GQA: Training generalized multi-query transformer models from multi-head checkpoints. In H. Bouamor, J. Pino, & K. Bali (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 4895–4901). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.298
  7. Alammar, J., & Grootendorst, M. (2024). Hands-on large language models: Language understanding and generation. O’Reilly Media.
  8. Alemohammad, S., Casco-Rodriguez, J., Luzi, L., Humayun, A. I., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R. G. (2024). Self-consuming generative models go MAD. In 12th International Conference on Learning Representations (ICLR 2024) (pp. 53581–53608). https://proceedings.iclr.cc/paper_files/paper/2024/hash/ebc042e767de551803ccfcc45e2454f5-Abstract-Conference.html
  9. Amodei, D. (2025, January). On DeepSeek and export controls. https://www.darioamodei.com/post/on-deepseek-and-export-controls
  10. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety (arXiv:1606.06565). arXiv. https://doi.org/10.48550/arXiv.1606.06565
  11. Anderson, P. W. (1972). More is different. Science, 177(4047), 393–396. https://doi.org/10.1126/science.177.4047.393
  12. Andreessen, M. (2023, October 16). The techno-optimist manifesto. Andreessen Horowitz. https://a16z.com/the-techno-optimist-manifesto/
  13. Anthropic Copyright Settlement. (n.d.). Frequently asked questions. Retrieved October 1, 2026, from https://www.anthropiccopyrightsettlement.com/faq
  14. Anthropic. (2023a, July 11). Claude 2. https://www.anthropic.com/news/claude-2
  15. Anthropic. (2023b, May 9). Claude's constitution. https://www.anthropic.com/news/claudes-constitution
  16. Anthropic. (2023c, September 19). Introducing Anthropic's Responsible Scaling Policy. https://www.anthropic.com/news/anthropics-responsible-scaling-policy
  17. Anthropic. (2023d, March 14). Introducing Claude. https://www.anthropic.com/news/introducing-claude
  18. Anthropic. (2024a, June 8). Claude's character. https://www.anthropic.com/research/claude-character
  19. Anthropic. (2024b, May 23). Golden Gate Claude. https://www.anthropic.com/news/golden-gate-claude
  20. Anthropic. (2024c, October 22). Introducing computer use, a new Claude 3.5 Sonnet, and Claude 3.5 Haiku. https://www.anthropic.com/news/3-5-models-and-computer-use
  21. Anthropic. (2024d, March 4). Introducing the next generation of Claude. https://www.anthropic.com/news/claude-3-family
  22. Anthropic. (2025a, February 24). Claude 3.7 Sonnet and Claude Code. https://www.anthropic.com/news/claude-3-7-sonnet
  23. Anthropic. (2025b, May). System card: Claude Opus 4 & Claude Sonnet 4. https://www.anthropic.com/claude-4-system-card
  24. Anthropic. (2026a, June 9). Claude Fable 5 and Claude Mythos 5. https://www.anthropic.com/news/claude-fable-5-mythos-5
  25. Anthropic. (2026b, January 22). Claude's new constitution. https://www.anthropic.com/news/claude-new-constitution
  26. Anthropic. (2026c, September 22). Introducing Claude Opus 5.5. https://www.anthropic.com/claude-opus-5-5
  27. Anthropic. (2026d, June 30). Redeploying Claude Fable 5. https://www.anthropic.com/news/redeploying-fable-5
  28. Anthropic. (n.d.). Models overview. Claude Platform Docs. Retrieved October 1, 2026, from https://platform.claude.com/docs/en/about-claude/models/overview
  29. Arai, Y., & Tsugawa, S. (2025). Do large language models advocate for inferentialism? (arXiv:2412.14501). arXiv. https://doi.org/10.48550/arXiv.2412.14501
  30. Arriola, M., Gokaslan, A., Chiu, J. T., Yang, Z., Qi, Z., Han, J., Sahoo, S. S., & Kuleshov, V. (2025). Block diffusion: Interpolating between autoregressive and diffusion language models. In The Thirteenth International Conference on Learning Representations. https://openreview.net/forum?id=tyEyYT267x
  31. Arrow, K. J. (1951). Social choice and individual values. Wiley. https://cowles.yale.edu/sites/default/files/2022-09/m12-all.pdf
  32. Austin, J., Johnson, D. D., Ho, J., Tarlow, D., & van den Berg, R. (2021). Structured denoising diffusion models in discrete state-spaces. In Advances in Neural Information Processing Systems (Vol. 34, pp. 17981–17993). Curran Associates. https://proceedings.neurips.cc/paper/2021/hash/958c530554f78bcd8e97125b70e6973d-Abstract.html
  33. Aycock, S., Stap, D., Wu, D., Monz, C., & Sima'an, K. (2025). Can LLMs really learn to translate a low-resource language from one grammar book? In 13th International Conference on Learning Representations (ICLR 2025) (pp. 12334–12357). https://proceedings.iclr.cc/paper_files/paper/2025/hash/20f44da80080d76bbc35bca0027f14e6-Abstract-Conference.html

B

  1. Ba, J. L., Kiros, J. R., & Hinton, G. E. (2016). Layer normalization (arXiv:1607.06450). arXiv. https://doi.org/10.48550/arXiv.1607.06450
  2. Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. In 3rd International Conference on Learning Representations (ICLR 2015). https://doi.org/10.48550/arXiv.1409.0473
  3. Baherwani, V., Chen, Z., Qiu, S., Wilson, A. G., & Izmailov, P. (2026). Emergent capabilities arise randomly from learning sparse attention patterns (arXiv:2606.25010). arXiv. https://doi.org/10.48550/arXiv.2606.25010
  4. Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., Chen, C., Olsson, C., Olah, C., Hernandez, D., Drain, D., Ganguli, D., Li, D., Tran-Johnson, E., Perez, E., . . . Kaplan, J. (2022). Constitutional AI: Harmlessness from AI feedback (arXiv:2212.08073). arXiv. https://doi.org/10.48550/arXiv.2212.08073
  5. Baker, B., Huizinga, J., Gao, L., Dou, Z., Guan, M. Y., Madry, A., Zaremba, W., Pachocki, J., & Farhi, D. (2025). Monitoring reasoning models for misbehavior and the risks of promoting obfuscation (arXiv:2503.11926). arXiv. https://doi.org/10.48550/arXiv.2503.11926
  6. Bandy, J., & Vincent, N. (2021). Addressing "documentation debt" in machine learning: A retrospective datasheet for BookCorpus. In J. Vanschoren & S. Yeung (Eds.), Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (Vol. 1). https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/hash/54229abfcfa5649e7003b83dd4755294-Abstract-round1.html
  7. Bar-Hillel, Y. (1960). The present status of automatic translation of languages. In F. L. Alt (Ed.), Advances in computers (Vol. 1, pp. 91–163). Academic Press. https://doi.org/10.1016/S0065-2458(08)60607-5
  8. Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge University Press.
  9. Bartz v. Anthropic PBC, No. C 24-05417 WHA (N.D. Cal. June 23, 2025) (order on fair use). https://storage.courtlistener.com/recap/gov.uscourts.cand.16/gov.uscourts.cand.16.231.0_1.pdf
  10. Bavarian, M., Jun, H., Tezak, N., Schulman, J., McLeavey, C., Tworek, J., & Chen, M. (2022). Efficient training of language models to fill in the middle (arXiv:2207.14255). arXiv. https://doi.org/10.48550/arXiv.2207.14255
  11. Becker, J., Rush, N., Barnes, E., & Rein, D. (2025). Measuring the impact of early-2025 AI on experienced open-source developer productivity (arXiv:2507.09089). arXiv. https://doi.org/10.48550/arXiv.2507.09089
  12. Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5185–5198). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.463
  13. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? 🦜. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922
  14. Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137–1155. https://www.jmlr.org/papers/v3/bengio03a.html
  15. Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166. https://doi.org/10.1109/72.279181
  16. Berglund, L., Tong, M., Kaufmann, M., Balesni, M., Stickland, A. C., Korbak, T., & Evans, O. (2024). The reversal curse: LLMs trained on “A is B” fail to learn “B is A”. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=GPKTIktA0k
  17. Berko, J. (1958). The child's learning of English morphology. Word, 14(2–3), 150–177. https://doi.org/10.1080/00437956.1958.11659661
  18. Betley, J., Warncke, N., Sztyber-Betley, A., Tan, D., Bao, X., Soto, M., Srivastava, M., Labenz, N., & Evans, O. (2026). Training large language models on narrow tasks can lead to broad misalignment. Nature, 649(8097), 584–589. https://doi.org/10.1038/s41586-025-09937-5
  19. Block, N. (1978). Troubles with functionalism. In C. W. Savage (Ed.), Perception and cognition: Issues in the foundations of psychology (pp. 261–325). University of Minnesota Press. https://hdl.handle.net/11299/185298
  20. Block, N. (1981). Psychologism and behaviorism. The Philosophical Review, 90(1), 5–43. https://doi.org/10.2307/2184371
  21. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., . . . Liang, P. (2021). On the opportunities and risks of foundation models (arXiv:2108.07258). arXiv. https://doi.org/10.48550/arXiv.2108.07258
  22. Boole, G. (1854). An investigation of the laws of thought, on which are founded the mathematical theories of logic and probabilities. Walton and Maberly. https://doi.org/10.5962/bhl.title.29413
  23. Borges, J. L. (1944). Funes el memorioso. In Ficciones. Sur. (Original work published 1942)
  24. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (pp. 144–152). Association for Computing Machinery. https://doi.org/10.1145/130385.130401
  25. Bostrom, N. (2002). Existential risks: Analyzing human extinction scenarios and related hazards. Journal of Evolution and Technology, 9(1). https://www.jetpress.org/volume9/risks.html
  26. Bostrom, N. (2003). Ethical issues in advanced artificial intelligence. In I. Smit, W. Wallach, & G. E. Lasker (Eds.), Cognitive, emotive and ethical aspects of decision making in humans and in artificial intelligence (Vol. 2, pp. 12–17). International Institute for Advanced Studies in Systems Research and Cybernetics.
  27. Bostrom, N. (2009). Pascal’s mugging. Analysis, 69(3), 443–445. https://doi.org/10.1093/analys/anp062
  28. Bostrom, N. (2012). The superintelligent will: Motivation and instrumental rationality in advanced artificial agents. Minds and Machines, 22(2), 71–85. https://doi.org/10.1007/s11023-012-9281-3
  29. Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
  30. Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015). A large annotated corpus for learning natural language inference. In L. Màrquez, C. Callison-Burch, & J. Su (Eds.), Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 632–642). Association for Computational Linguistics. https://doi.org/10.18653/v1/D15-1075
  31. Bradley, R. A., & Terry, M. E. (1952). Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3–4), 324–345. https://doi.org/10.1093/biomet/39.3-4.324
  32. Brandom, R. B. (1994). Making it explicit: Reasoning, representing, and discursive commitment. Harvard University Press.
  33. Brandom, R. B. (2000). Articulating reasons: An introduction to inferentialism. Harvard University Press.
  34. Branwen, G. (2020, May 28). The scaling hypothesis. Gwern.net. https://gwern.net/scaling-hypothesis
  35. Brentano, F. (1995). Psychology from an empirical standpoint (O. Kraus & L. L. McAlister, Eds.; A. C. Rancurello, D. B. Terrell, & L. L. McAlister, Trans.). Routledge. (Original work published 1874)
  36. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., . . . Amodei, D. (2020). Language models are few-shot learners. In Advances in Neural Information Processing Systems (Vol. 33, pp. 1877–1901). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
  37. Brunila, M., & LaViolette, J. (2022). What company do words keep? Revisiting the distributional semantics of J.R. Firth & Zellig Harris. In M. Carpuat, M.-C. de Marneffe, & I. V. Meza Ruiz (Eds.), Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4403–4417). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.naacl-main.327
  38. Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4 (arXiv:2303.12712). arXiv. https://doi.org/10.48550/arXiv.2303.12712
  39. Buchanan, B. G., & Shortliffe, E. H. (Eds.). (1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley.
  40. Buckner, C. J. (2024). From deep learning to rational machines: What the history of philosophy can teach us about the future of artificial intelligence. Oxford University Press. https://doi.org/10.1093/oso/9780197653302.001.0001

C

  1. Callison-Burch, C., Osborne, M., & Koehn, P. (2006). Re-evaluating the role of BLEU in machine translation research. In D. McCarthy & S. Wintner (Eds.), 11th Conference of the European Chapter of the Association for Computational Linguistics (pp. 249–256). Association for Computational Linguistics. https://aclanthology.org/E06-1032/
  2. Campbell, D. T. (1979). Assessing the impact of planned social change. Evaluation and Program Planning, 2(1), 67–90. https://doi.org/10.1016/0149-7189(79)90048-X
  3. Campbell, M., Hoane, A. J., & Hsu, F.-H. (2002). Deep Blue. Artificial Intelligence, 134(1–2), 57–83. https://doi.org/10.1016/S0004-3702(01)00129-1
  4. Canaan, R., Salge, C., Togelius, J., & Nealen, A. (2019). Leveling the playing field: Fairness in AI versus human game benchmarks. In Proceedings of the 14th International Conference on the Foundations of Digital Games (pp. 1–8). Association for Computing Machinery. https://doi.org/10.1145/3337722.3337750
  5. Carey, S., & Bartlett, E. (1978). Acquiring a single new word. Papers and Reports on Child Language Development, 15, 17–29. https://eric.ed.gov/?id=ED198703
  6. Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramèr, F., & Zhang, C. (2023). Quantifying memorization across neural language models. In 11th International Conference on Learning Representations (ICLR 2023). https://openreview.net/forum?id=TatRHT_1cK
  7. Carlini, N., Tramèr, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, Ú., Oprea, A., & Raffel, C. (2021). Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21) (pp. 2633–2650). USENIX Association. https://www.usenix.org/conference/usenixsecurity21/presentation/carlini-extracting
  8. Carnap, R. (1952). The continuum of inductive methods. University of Chicago Press.
  9. Carroll, L. (1895). What the tortoise said to Achilles. Mind, 4(14), 278–280. https://doi.org/10.1093/mind/IV.14.278
  10. Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22. https://doi.org/10.1037/h0046743
  11. Chen, Y., Benton, J., Radhakrishnan, A., Uesato, J., Denison, C., Schulman, J., Somani, A., Hase, P., Wagner, M., Roger, F., Mikulik, V., Bowman, S. R., Leike, J., Kaplan, J., & Perez, E. (2025). Reasoning models don't always say what they think (arXiv:2505.05410). arXiv. https://doi.org/10.48550/arXiv.2505.05410
  12. Chesney, B., & Citron, D. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753–1820. https://doi.org/10.15779/Z38RV0D15J
  13. Chiang, T. (2023, February 9). ChatGPT is a blurry JPEG of the web. The New Yorker. https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web
  14. Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder–decoder for statistical machine translation. In A. Moschitti, B. Pang, & W. Daelemans (Eds.), Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1724–1734). Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1179
  15. Chollet, F. (2019). On the measure of intelligence (arXiv:1911.01547). arXiv. https://doi.org/10.48550/arXiv.1911.01547
  16. Chollet, F. (2021). Deep learning with Python (2nd ed.). Manning Publications.
  17. Chomsky, N. (1956). Three models for the description of language. IRE Transactions on Information Theory, 2(3), 113–124. https://doi.org/10.1109/TIT.1956.1056813
  18. Chomsky, N. (1957). Syntactic structures. Mouton. https://doi.org/10.1515/9783112316009
  19. Chomsky, N. (1959). [Review of the book Verbal behavior, by B. F. Skinner]. Language, 35(1), 26–58. https://doi.org/10.2307/411334
  20. Chomsky, N. (1965). Aspects of the theory of syntax. MIT Press.
  21. Chomsky, N. (1981). Lectures on government and binding: The Pisa lectures. Foris.
  22. Christiano, P. (2018, February 24). Takeoff speeds. The Sideways View. https://sideways-view.com/2018/02/24/takeoff-speeds/
  23. Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. In Advances in Neural Information Processing Systems (Vol. 30). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html
  24. Church, A. (1936). An unsolvable problem of elementary number theory. American Journal of Mathematics, 58(2), 345–363. https://doi.org/10.2307/2371045
  25. Churchland, P. M., & Churchland, P. S. (1990). Could a machine think? Scientific American, 262(1), 32–37. https://doi.org/10.1038/scientificamerican0190-32
  26. Clark, A. (2025). Extending minds with generative AI. Nature Communications, 16, Article 4627. https://doi.org/10.1038/s41467-025-59906-9
  27. Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7
  28. Clark, K., Khandelwal, U., Levy, O., & Manning, C. D. (2019). What does BERT look at? An analysis of BERT’s attention. In T. Linzen, G. Chrupała, Y. Belinkov, & D. Hupkes (Eds.), Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (pp. 276–286). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4828
  29. Coelho Mollo, D., & Millière, R. (2026). The vector grounding problem. Philosophy and the Mind Sciences, 7(1). https://doi.org/10.33735/phimisci.2026.12307
  30. Coldewey, D. (2023, December 7). Google's best Gemini demo was faked. TechCrunch. https://techcrunch.com/2023/12/07/googles-best-gemini-demo-was-faked/
  31. CompVis. (2022). Stable Diffusion [Computer software]. GitHub. https://github.com/CompVis/stable-diffusion
  32. Conneau, A., Rinott, R., Lample, G., Williams, A., Bowman, S., Schwenk, H., & Stoyanov, V. (2018). XNLI: Evaluating cross-lingual sentence representations. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 2475–2485). Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1269
  33. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
  34. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964
  35. Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. https://doi.org/10.1037/h0040957
  36. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), 303–314. https://doi.org/10.1007/BF02551274

D

  1. Dagan, I., Glickman, O., & Magnini, B. (2006). The PASCAL recognising textual entailment challenge. In J. Quiñonero-Candela, I. Dagan, B. Magnini, & F. d’Alché-Buc (Eds.), Machine learning challenges: Evaluating predictive uncertainty, visual object classification, and recognising textual entailment (pp. 177–190). Springer. https://doi.org/10.1007/11736790_9
  2. Dai, D., Deng, C., Zhao, C., Xu, R. X., Gao, H., Chen, D., Li, J., Zeng, W., Yu, X., Wu, Y., Xie, Z., Li, Y. K., Huang, P., Luo, F., Ruan, C., Sui, Z., & Liang, W. (2024). DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1280–1297). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.70
  3. Dao, T., & Gu, A. (2024). Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality (arXiv:2405.21060). arXiv. https://doi.org/10.48550/arXiv.2405.21060
  4. de Freitas, N. [@NandoDF]. (2022, May 14). Someone’s opinion article. My opinion: It’s all about scale now! The Game is Over! It’s about making these models bigger [Post]. X. https://x.com/NandoDF/status/1525397036325019649
  5. De, S., Smith, S. L., Fernando, A., Botev, A., Cristian-Muraru, G., Gu, A., Haroun, R., Berrada, L., Chen, Y., Srinivasan, S., Desjardins, G., Doucet, A., Budden, D., Teh, Y. W., Pascanu, R., De Freitas, N., & Gulcehre, C. (2024). Griffin: Mixing gated linear recurrences with local attention for efficient language models (arXiv:2402.19427). arXiv. https://doi.org/10.48550/arXiv.2402.19427
  6. DeepSeek-AI. (2024). DeepSeek-V3 technical report (arXiv:2412.19437). arXiv. https://doi.org/10.48550/arXiv.2412.19437
  7. DeepSeek. (2024, December 26). Introducing DeepSeek-V3. DeepSeek API Docs. https://api-docs.deepseek.com/news/news1226
  8. DeepSeek. (2025, January 20). DeepSeek-R1 release. DeepSeek API Docs. https://api-docs.deepseek.com/news/news250120
  9. DeepSeek. (2026, April 24). DeepSeek V4 preview release. DeepSeek API Docs. https://api-docs.deepseek.com/news/news260424
  10. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). IEEE. https://doi.org/10.1109/CVPR.2009.5206848
  11. Dennett, D. C. (1987). The intentional stance. MIT Press.
  12. Dettmers, T., Lewis, M., Belkada, Y., & Zettlemoyer, L. (2022). LLM.int8(): 8-bit matrix multiplication for transformers at scale. In Advances in Neural Information Processing Systems (Vol. 35, pp. 30318–30332). Curran Associates. https://doi.org/10.52202/068431-2198
  13. Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. In Advances in Neural Information Processing Systems (Vol. 36, pp. 10088–10115). Curran Associates. https://doi.org/10.52202/075280-0441
  14. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423
  15. Donahue, C., Lee, M., & Liang, P. (2020). Enabling language models to fill in the blanks. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 2492–2501). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.225
  16. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In 9th International Conference on Learning Representations (ICLR 2021). https://openreview.net/forum?id=YicbFdNTTy
  17. Dupuis, N., & janus. (2023, February 10). Cyborgism. LessWrong. https://www.lesswrong.com/posts/bxt7uCiHam4QXrQAA/cyborgism

E

  1. Edman, L., Schmid, H., & Fraser, A. (2024). CUTE: Measuring LLMs’ understanding of their tokens. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 3017–3026). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.177
  2. Edwards, B. (2023, February 10). AI-powered Bing Chat spills its secrets via prompt injection attack. Ars Technica. https://arstechnica.com/information-technology/2023/02/ai-powered-bing-chat-spills-its-secrets-via-prompt-injection-attack/
  3. Elhage, N., Hume, T., Olsson, C., Schiefer, N., Henighan, T., Kravec, S., Hatfield-Dodds, Z., Lasenby, R., Drain, D., Chen, C., Grosse, R., McCandlish, S., Kaplan, J., Amodei, D., Wattenberg, M., & Olah, C. (2022). Toy models of superposition. Transformer Circuits Thread. https://transformer-circuits.pub/2022/toy_model/index.html
  4. Elhage, N., Nanda, N., Olsson, C., Henighan, T., Joseph, N., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., DasSarma, N., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Jones, A., Kernion, J., Lovitt, L., Ndousse, K., . . . Olah, C. (2021). A mathematical framework for transformer circuits. Transformer Circuits Thread. https://transformer-circuits.pub/2021/framework/index.html
  5. Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1207/s15516709cog1402_1
  6. Engels, J., Michaud, E. J., Liao, I., Gurnee, W., & Tegmark, M. (2025). Not all language model features are one-dimensionally linear. In 13th International Conference on Learning Representations (ICLR 2025) (pp. 84591–84622). https://proceedings.iclr.cc/paper_files/paper/2025/hash/d3221cdb27e49d9c1cd35ad254feccfe-Abstract-Conference.html
  7. Epoch AI. (2026). Data on AI models [Data set]. Retrieved October 1, 2026, from https://epoch.ai/data/ai-models
  8. Evans, R., Saxton, D., Amos, D., Kohli, P., & Grefenstette, E. (2018). Can neural networks understand logical entailment? In 6th International Conference on Learning Representations (ICLR 2018). https://openreview.net/forum?id=SkZxCk-0Z
  9. Exec. Order No. 14110, 88 Fed. Reg. 75191 (Nov. 1, 2023). https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence

F

  1. Fallis, D. (2021). The epistemic threat of deepfakes. Philosophy & Technology, 34(4), 623–643. https://doi.org/10.1007/s13347-020-00419-2
  2. Feldman, V. (2020). Does learning require memorization? A short tale about a long tail. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing (STOC '20) (pp. 954–959). Association for Computing Machinery. https://doi.org/10.1145/3357713.3384290
  3. Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In D. Precup & Y. W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning (Vol. 70, pp. 1126–1135). PMLR. https://proceedings.mlr.press/v70/finn17a.html
  4. Firestone, C. (2020). Performance vs. competence in human–machine comparisons. Proceedings of the National Academy of Sciences, 117(43), 26562–26571. https://doi.org/10.1073/pnas.1905334117
  5. Firth, J. R. (1957). A synopsis of linguistic theory, 1930–1955. In Studies in linguistic analysis (pp. 1–32). Blackwell.
  6. Fix, E., & Hodges, J. L., Jr. (1951). Discriminatory analysis. Nonparametric discrimination: Consistency properties (Report No. 4). USAF School of Aviation Medicine. https://doi.org/10.1037/e471672008-001
  7. Floridi, L., Jia, Y., & Tohmé, F. (2025). A categorical analysis of large language models and why LLMs circumvent the symbol grounding problem (arXiv:2512.09117). arXiv. https://doi.org/10.48550/arXiv.2512.09117
  8. Floridi, L., Morley, J., Novelli, C., & Watson, D. (2025). What kind of reasoning (if any) is an LLM actually doing? On the stochastic nature and abductive appearance of large language models (arXiv:2512.10080). arXiv. https://doi.org/10.48550/arXiv.2512.10080
  9. Fodor, J. A. (1975). The language of thought. Thomas Y. Crowell.
  10. Fodor, J. A. (1983). The modularity of mind: An essay on faculty psychology. MIT Press. https://doi.org/10.7551/mitpress/4737.001.0001
  11. Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1–2), 3–71. https://doi.org/10.1016/0010-0277(88)90031-5
  12. Fodor, J., & Lepore, E. (1996). The red herring and the pet fish: Why concepts still can't be prototypes. Cognition, 58(2), 253–270. https://doi.org/10.1016/0010-0277(95)00694-X
  13. Forster, M., & Sober, E. (1994). How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions. The British Journal for the Philosophy of Science, 45(1), 1–35. https://doi.org/10.1093/bjps/45.1.1
  14. Francis, W. N., & Kučera, H. (1979). Manual of information to accompany A Standard Corpus of Present-Day Edited American English, for use with digital computers (Rev. and amplified ed.). Department of Linguistics, Brown University. http://korpus.uib.no/icame/manuals/BROWN/INDEX.HTM (Original work published 1964)
  15. Frege, G. (1884). Die Grundlagen der Arithmetik: Eine logisch mathematische Untersuchung über den Begriff der Zahl [The foundations of arithmetic: A logico-mathematical enquiry into the concept of number]. W. Koebner.
  16. Frege, G. (1998). Sobre sentido y referencia [On sense and reference] (L. M. Valdés Villanueva, Trans.). In L. M. Valdés Villanueva (Ed.), Ensayos de semántica y filosofía de la lógica (pp. 84–111). Tecnos. (Original work published 1892)
  17. French, R. M. (1999). Catastrophic forgetting in connectionist networks. Trends in Cognitive Sciences, 3(4), 128–135. https://doi.org/10.1016/S1364-6613(99)01294-2
  18. Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 193–202. https://doi.org/10.1007/BF00344251

G

  1. Gage, P. (1994). A new algorithm for data compression. The C Users Journal, 12(2), 23–38.
  2. Galton, F. (1886). Regression towards mediocrity in hereditary stature. The Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263. https://doi.org/10.2307/2841583
  3. Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., & Leahy, C. (2020). The Pile: An 800GB dataset of diverse text for language modeling (arXiv:2101.00027). arXiv. https://doi.org/10.48550/arXiv.2101.00027
  4. Gao, L., Schulman, J., & Hilton, J. (2023). Scaling laws for reward model overoptimization. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10835–10866). PMLR. https://proceedings.mlr.press/v202/gao23h.html
  5. Gärdenfors, P. (2000). Conceptual spaces: The geometry of thought. MIT Press. https://doi.org/10.7551/mitpress/2076.001.0001
  6. Gebru, T., & Torres, É. P. (2024). The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence. First Monday, 29(4). https://doi.org/10.5210/fm.v29i4.13636
  7. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., III, & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
  8. Geiger, A., Lu, H., Icard, T., & Potts, C. (2021). Causal abstractions of neural networks. In Advances in Neural Information Processing Systems (Vol. 34, pp. 9574–9586). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2021/hash/4f5c422f4d49a5a807eda27434231040-Abstract.html
  9. Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11), 665–673. https://doi.org/10.1038/s42256-020-00257-z
  10. Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., & Brendel, W. (2019). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In 7th International Conference on Learning Representations (ICLR 2019). https://openreview.net/forum?id=Bygh9j09KX
  11. Gemini Team. (2023). Gemini: A family of highly capable multimodal models (arXiv:2312.11805). arXiv. https://doi.org/10.48550/arXiv.2312.11805
  12. Gemini Team. (2024). Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (arXiv:2403.05530). arXiv. https://doi.org/10.48550/arXiv.2403.05530
  13. Gemini Team. (2025). Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities (arXiv:2507.06261). arXiv. https://doi.org/10.48550/arXiv.2507.06261
  14. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10), 2451–2471. https://doi.org/10.1162/089976600300015015
  15. Gerstgrasser, M., Schaeffer, R., Dey, A., Rafailov, R., Sleight, H., Hughes, J., Korbak, T., Agrawal, R., Pai, D., Gromov, A., Roberts, D. A., Yang, D., Donoho, D. L., & Koyejo, S. (2024). Is model collapse inevitable? Breaking the curse of recursion by accumulating real and synthetic data (arXiv:2404.01413). arXiv. https://doi.org/10.48550/arXiv.2404.01413
  16. Geva, M., Schuster, R., Berant, J., & Levy, O. (2021). Transformer feed-forward layers are key-value memories. In M.-F. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 5484–5495). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.446
  17. Good, I. J. (1966). Speculations concerning the first ultraintelligent machine. In F. L. Alt & M. Rubinoff (Eds.), Advances in computers (Vol. 6, pp. 31–88). Academic Press. https://doi.org/10.1016/S0065-2458(08)60418-0
  18. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (Vol. 27). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2014/hash/f033ed80deb0234979a61f95710dbe25-Abstract.html
  19. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. In 3rd International Conference on Learning Representations (ICLR 2015). https://doi.org/10.48550/arXiv.1412.6572
  20. Goodhart, C. A. E. (1975). Problems of monetary management: The U.K. experience. In Papers in monetary economics (Vol. 1). Reserve Bank of Australia.
  21. Goodman, N. (1972). Seven strictures on similarity. In Problems and projects (pp. 437–446). Bobbs-Merrill.
  22. Goodman, N. (1983). Fact, fiction, and forecast (4th ed.). Harvard University Press. (Original work published 1954)
  23. Google DeepMind. (2025, May 20). Gemini Diffusion is our new experimental research model. The Keyword. https://blog.google/innovation-and-ai/models-and-research/google-deepmind/gemini-diffusion/
  24. Google DeepMind. (2026). DiffusionGemma model card. Google AI for Developers. Retrieved October 11, 2026, from https://ai.google.dev/gemma/docs/diffusiongemma/model_card
  25. Google. (2026). Diffusion in text generation explained. Google AI for Developers. Retrieved October 12, 2026, from https://ai.google.dev/gemma/docs/diffusiongemma/explained
  26. Google. (n.d.). Release notes: Gemini API. Google AI for Developers. Retrieved October 1, 2026, from https://ai.google.dev/gemini-api/docs/changelog
  27. Grace, K., Sandkühler, J. F., Stewart, H., Weinstein-Raun, B., Thomas, S., Stein-Perlman, Z., Salvatier, J., Brauner, J., & Korzekwa, R. C. (2025). Thousands of AI authors on the future of AI. Journal of Artificial Intelligence Research, 84, Article 9. https://doi.org/10.1613/jair.1.19087
  28. Graves, A., Mohamed, A.-R., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 6645–6649). IEEE. https://doi.org/10.1109/ICASSP.2013.6638947
  29. Gray, M. L., & Suri, S. (2019). Ghost work: How to stop Silicon Valley from building a new global underclass. Houghton Mifflin Harcourt.
  30. Greenblatt, R., Denison, C., Wright, B., Roger, F., MacDiarmid, M., Marks, S., Treutlein, J., Belonax, T., Chen, J., Duvenaud, D., Khan, A., Michael, J., Mindermann, S., Perez, E., Petrini, L., Uesato, J., Kaplan, J., Shlegeris, B., Bowman, S. R., & Hubinger, E. (2024). Alignment faking in large language models (arXiv:2412.14093). arXiv. https://doi.org/10.48550/arXiv.2412.14093
  31. Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., & Fritz, M. (2023). Not what you've signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection. In Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security (AISec '23) (pp. 79–90). Association for Computing Machinery. https://doi.org/10.1145/3605764.3623985
  32. Grice, H. P. (1957). Meaning. The Philosophical Review, 66(3), 377–388. https://doi.org/10.2307/2182440
  33. Grice, H. P. (1975). Logic and conversation. In P. Cole & J. L. Morgan (Eds.), Syntax and semantics: Vol. 3. Speech acts (pp. 41–58). Academic Press. https://doi.org/10.1163/9789004368811_003
  34. Gu, A., & Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces (arXiv:2312.00752). arXiv. https://doi.org/10.48550/arXiv.2312.00752
  35. Gulordava, K., Bojanowski, P., Grave, E., Linzen, T., & Baroni, M. (2018). Colorless green recurrent networks dream hierarchically. In M. Walker, H. Ji, & A. Stent (Eds.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 1195–1205). Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-1108
  36. Guo, D., Yang, D., Zhang, H., Song, J., Wang, P., Zhu, Q., Xu, R., Zhang, R., Ma, S., Bi, X., Zhang, X., Yu, X., Wu, Y., Wu, Z. F., Gou, Z., Shao, Z., Li, Z., Gao, Z., Liu, A., . . . Zhang, Z. (2025). DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. Nature, 645(8081), 633–638. https://doi.org/10.1038/s41586-025-09422-z
  37. Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S. R., & Smith, N. A. (2018). Annotation artifacts in natural language inference data. In M. Walker, H. Ji, & A. Stent (Eds.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) (pp. 107–112). Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-2017
  38. Gutmann, M., & Hyvärinen, A. (2010). Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Y. W. Teh & M. Titterington (Eds.), Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Vol. 9, pp. 297–304). PMLR. https://proceedings.mlr.press/v9/gutmann10a.html

H

  1. Hadfield-Menell, D., Dragan, A., Abbeel, P., & Russell, S. (2017). The off-switch game. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) (pp. 220–227). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2017/32
  2. Hanson, R., & Yudkowsky, E. (2013). The Hanson-Yudkowsky AI-Foom debate. Machine Intelligence Research Institute. https://intelligence.org/files/AIFoomDebate.pdf
  3. Harlow, H. F. (1949). The formation of learning sets. Psychological Review, 56(1), 51–65. https://doi.org/10.1037/h0062474
  4. Harman, G., & Kulkarni, S. (2007). Reliable reasoning: Induction and statistical learning theory. MIT Press. https://doi.org/10.7551/mitpress/5876.001.0001
  5. Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1–3), 335–346. https://doi.org/10.1016/0167-2789(90)90087-6
  6. Harper, D. (n.d.). Benchmark (n.). In Online Etymology Dictionary. Retrieved October 1, 2026, from https://www.etymonline.com/word/benchmark
  7. Harris, Z. S. (1954). Distributional structure. Word, 10(2–3), 146–162. https://doi.org/10.1080/00437956.1954.11659520
  8. Harris, Z. S. (1955). From phoneme to morpheme. Language, 31(2), 190–222. https://doi.org/10.2307/411036
  9. Hase, P., Bansal, M., Kim, B., & Ghandeharioun, A. (2023). Does localization inform editing? Surprising differences in causality-based localization vs. knowledge editing in language models. In Advances in Neural Information Processing Systems (Vol. 36, pp. 17643–17668). Curran Associates. https://doi.org/10.52202/075280-0774
  10. Haugeland, J. (1985). Artificial intelligence: The very idea. MIT Press.
  11. Havlík, V. (2025). Meaning and understanding in large language models. Synthese, 205(1), Article 9. https://doi.org/10.1007/s11229-024-04878-4
  12. Hayes, A., Li, B. Z., & Andreas, J. (2026). Explaining attention with program synthesis (arXiv:2606.19317). arXiv. https://doi.org/10.48550/arXiv.2606.19317
  13. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778). IEEE. https://doi.org/10.1109/CVPR.2016.90
  14. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. Wiley.
  15. Heider, F., & Simmel, M. (1944). An experimental study of apparent behavior. The American Journal of Psychology, 57(2), 243–259. https://doi.org/10.2307/1416950
  16. Hempel, C. G. (1965). Aspects of scientific explanation and other essays in the philosophy of science. Free Press.
  17. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2021). Measuring massive multitask language understanding. In 9th International Conference on Learning Representations (ICLR 2021). https://openreview.net/forum?id=d7KBjmI3GmQ
  18. Hernández-Orallo, J. (2017). The measure of all minds: Evaluating natural and artificial intelligence. Cambridge University Press. https://doi.org/10.1017/9781316594179
  19. Hewitt, J., & Liang, P. (2019). Designing and interpreting probes with control tasks. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 2733–2743). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1275
  20. Hewitt, J., & Manning, C. D. (2019). A structural probe for finding syntax in word representations. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4129–4138). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1419
  21. Hicks, M. T., Humphries, J., & Slater, J. (2024). ChatGPT is bullshit. Ethics and Information Technology, 26(2), Article 38. https://doi.org/10.1007/s10676-024-09775-5
  22. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network (arXiv:1503.02531). arXiv. https://doi.org/10.48550/arXiv.1503.02531
  23. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (Vol. 33, pp. 6840–6851). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html
  24. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  25. Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., de Las Casas, D., Hendricks, L. A., Welbl, J., Clark, A., Hennigan, T., Noland, E., Millican, K., van den Driessche, G., Damoc, B., Guy, A., Osindero, S., Simonyan, K., Elsen, E., . . . Sifre, L. (2022). Training compute-optimal large language models. In Advances in Neural Information Processing Systems (Vol. 35, pp. 30016–30030). Curran Associates. https://doi.org/10.52202/068431-2176
  26. Hofstadter, D. R., & Fluid Analogies Research Group. (1995). Fluid concepts and creative analogies: Computer models of the fundamental mechanisms of thought. Basic Books.
  27. Holliday, W. H., Mandelkern, M., & Zhang, C. E. (2024). Conditional and modal reasoning in large language models. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 3800–3821). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.222
  28. Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2020). The curious case of neural text degeneration. In 8th International Conference on Learning Representations (ICLR 2020). https://openreview.net/forum?id=rygGQyrFvH
  29. Hooker, S. (2021). The hardware lottery. Communications of the ACM, 64(12), 58–65. https://doi.org/10.1145/3467017
  30. Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558. https://doi.org/10.1073/pnas.79.8.2554
  31. Hopfield, J. J., & Tank, D. W. (1985). “Neural” computation of decisions in optimization problems. Biological Cybernetics, 52(3), 141–152. https://doi.org/10.1007/BF00339943
  32. Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2), 251–257. https://doi.org/10.1016/0893-6080(91)90009-T
  33. Houlsby, N., Giurgiu, A., Jastrzębski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., & Gelly, S. (2019). Parameter-efficient transfer learning for NLP. In K. Chaudhuri & R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 2790–2799). PMLR. https://proceedings.mlr.press/v97/houlsby19a.html
  34. Hsiao, S. (2024, February 8). Bard becomes Gemini: Try Ultra 1.0 and a new mobile app today. The Keyword. https://blog.google/products/gemini/bard-gemini-advanced-app/
  35. Hsu, F.-H. (1999). IBM’s Deep Blue chess grandmaster chips. IEEE Micro, 19(2), 70–81. https://doi.org/10.1109/40.755469
  36. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). LoRA: Low-rank adaptation of large language models. In 10th International Conference on Learning Representations (ICLR 2022). https://openreview.net/forum?id=nZeVKeeFYf9
  37. Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1), 106–154. https://doi.org/10.1113/jphysiol.1962.sp006837
  38. Hubinger, E., van Merwijk, C., Mikulik, V., Skalse, J., & Garrabrant, S. (2019). Risks from learned optimization in advanced machine learning systems (arXiv:1906.01820). arXiv. https://doi.org/10.48550/arXiv.1906.01820
  39. Hume, D. (2007). An enquiry concerning human understanding (P. Millican, Ed.). Oxford University Press. (Original work published 1748)

I

  1. Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., & Madry, A. (2019). Adversarial examples are not bugs, they are features. In Advances in Neural Information Processing Systems (Vol. 32). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2019/hash/e2c420d928d4bf8ce0ff2ec19b371514-Abstract.html
  2. Inception Labs, Khanna, S., Kharbanda, S., Li, S., Varma, H., Wang, E., Birnbaum, S., Luo, Z., Miraoui, Y., Palrecha, A., Ermon, S., Grover, A., & Kuleshov, V. (2025). Mercury: Ultra-fast language models based on diffusion (arXiv:2506.17298). arXiv. https://doi.org/10.48550/arXiv.2506.17298
  3. Instituto Geográfico Nacional. (n.d.). Nivelación: Introducción. Retrieved October 1, 2026, from https://www.ign.gob.ar/NuestrasActividades/Geodesia/Nivelacion/Introduccion

J

  1. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Computation, 3(1), 79–87. https://doi.org/10.1162/neco.1991.3.1.79
  2. Jain, S., & Wallace, B. C. (2019). Attention is not explanation. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 3543–3556). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1357
  3. janus. (2021, February 9). Loom: Interface to the multiverse. generative.ink. https://generative.ink/posts/loom-interface-to-the-multiverse/
  4. janus. (2022, September 2). Simulators. LessWrong. https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators
  5. Jelassi, S., Brandfonbrener, D., Kakade, S. M., & Malach, E. (2024). Repeat after me: Transformers are better than state space models at copying. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 21502–21521). PMLR. https://proceedings.mlr.press/v235/jelassi24a.html
  6. Jelinek, F., Mercer, R. L., Bahl, L. R., & Baker, J. K. (1977). Perplexity—a measure of the difficulty of speech recognition tasks. The Journal of the Acoustical Society of America, 62(S1), S63. https://doi.org/10.1121/1.2016299
  7. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730
  8. Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., de Las Casas, D., Bou Hanna, E., Bressand, F., Lengyel, G., Bour, G., Lample, G., Renard Lavaud, L., Saulnier, L., Lachaux, M.-A., Stock, P., Subramanian, S., Yang, S., . . . El Sayed, W. (2024). Mixtral of experts (arXiv:2401.04088). arXiv. https://doi.org/10.48550/arXiv.2401.04088
  9. Jones, C. R., & Bergen, B. K. (2026). Large language models pass a standard three-party Turing test. Proceedings of the National Academy of Sciences, 123(21), Article e2524472123. https://doi.org/10.1073/pnas.2524472123
  10. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., . . . Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2

K

  1. Kallini, J., & Potts, C. (2026). Language models as tools for investigating the distinction between possible and impossible natural languages. Behavioral and Brain Sciences, 49, Article e211. https://doi.org/10.1017/S0140525X26104737
  2. Kallini, J., Papadimitriou, I., Futrell, R., Mahowald, K., & Potts, C. (2024). Mission: Impossible language models. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 14691–14714). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.787
  3. Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020). Scaling laws for neural language models (arXiv:2001.08361). arXiv. https://doi.org/10.48550/arXiv.2001.08361
  4. Karpathy, A. [@karpathy]. (2025, February 2). There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials [Post]. X. https://x.com/karpathy/status/1886192184808149383
  5. Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4396–4405). IEEE. https://doi.org/10.1109/CVPR.2019.00453
  6. Katharopoulos, A., Vyas, A., Pappas, N., & Fleuret, F. (2020). Transformers are RNNs: Fast autoregressive transformers with linear attention. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 5156–5165). PMLR. https://proceedings.mlr.press/v119/katharopoulos20a.html
  7. Khandelwal, U., He, H., Qi, P., & Jurafsky, D. (2018). Sharp nearby, fuzzy far away: How neural language models use context. In I. Gurevych & Y. Miyao (Eds.), Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 284–294). Association for Computational Linguistics. https://doi.org/10.18653/v1/P18-1027
  8. Kimi Team. (2025). Kimi Linear: An expressive, efficient attention architecture (arXiv:2510.26692). arXiv. https://doi.org/10.48550/arXiv.2510.26692
  9. Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. In 2nd International Conference on Learning Representations (ICLR 2014). https://doi.org/10.48550/arXiv.1312.6114
  10. Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., & Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521–3526. https://doi.org/10.1073/pnas.1611835114
  11. Kitouni, O., Nolte, N., Bouchacourt, D., Williams, A., Rabbat, M., & Ibrahim, M. (2024). The factorization curse: Which tokens you predict underlie the reversal curse and more. In Advances in Neural Information Processing Systems (Vol. 37, pp. 112329–112355). Curran Associates. https://doi.org/10.52202/079017-3568
  12. Kleene, S. C. (1956). Representation of events in nerve nets and finite automata. In C. E. Shannon & J. McCarthy (Eds.), Automata studies (pp. 3–42). Princeton University Press. https://doi.org/10.1515/9781400882618-002
  13. Koenecke, A., Choi, A. S. G., Mei, K. X., Schellmann, H., & Sloane, M. (2024). Careless Whisper: Speech-to-text hallucination harms. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1672–1681). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658996
  14. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. In Advances in Neural Information Processing Systems (Vol. 35, pp. 22199–22213). Curran Associates. https://doi.org/10.52202/068431-1613
  15. Kokotajlo, D., Alexander, S., Larsen, T., Lifland, E., & Dean, R. (2025, April 3). AI 2027. https://ai-2027.com/
  16. Korbak, T., Balesni, M., Barnes, E., Bengio, Y., Benton, J., Bloom, J., Chen, M., Cooney, A., Dafoe, A., Dragan, A., Emmons, S., Evans, O., Farhi, D., Greenblatt, R., Hendrycks, D., Hobbhahn, M., Hubinger, E., Irving, G., Jenner, E., . . . Mikulik, V. (2025). Chain of thought monitorability: A new and fragile opportunity for AI safety (arXiv:2507.11473). arXiv. https://doi.org/10.48550/arXiv.2507.11473
  17. Kovatchev, V., & Taulé, M. (2022). InferES: A natural language inference corpus for Spanish featuring negation-based contrastive and adversarial examples. In Proceedings of the 29th International Conference on Computational Linguistics (pp. 3873–3884). International Committee on Computational Linguistics. https://aclanthology.org/2022.coling-1.340/
  18. Krakauer, D. C., Mitchell, M., & Krakauer, J. W. (2026). Large language models and emergence: A complex systems perspective. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 384(2320), Article 20250014. https://doi.org/10.1098/rsta.2025.0014
  19. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (Vol. 25). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
  20. Ku, A., Campbell, D., Bai, X., Geng, J., Liu, R., Marjieh, R., McCoy, R. T., Nam, A., Sucholutsky, I., Zhang, L., Zhu, J.-Q., & Griffiths, T. (2026). Levels of analysis for large language models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 384(2320), Article 20250012. https://doi.org/10.1098/rsta.2025.0012
  21. Kumaran, D., Hassabis, D., & McClelland, J. L. (2016). What learning systems do intelligent agents need? Complementary learning systems theory updated. Trends in Cognitive Sciences, 20(7), 512–534. https://doi.org/10.1016/j.tics.2016.05.004

L

  1. Lake, B. M., & Baroni, M. (2023). Human-like systematic generalization through a meta-learning neural network. Nature, 623(7985), 115–121. https://doi.org/10.1038/s41586-023-06668-3
  2. Lane, R., Hay, A., Schwarz, A., Berry, D. M., & Shrager, J. (2025). ELIZA reanimated: Restoring the mother of all chatbots to one of the world’s first time-sharing systems. IEEE Annals of the History of Computing, 47(2), 68–76. https://doi.org/10.1109/MAHC.2025.3564095
  3. Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., Li, D., Durmus, E., Hubinger, E., Kernion, J., Lukošiūtė, K., Nguyen, K., Cheng, N., Joseph, N., Schiefer, N., Rausch, O., Larson, R., McCandlish, S., Kundu, S., . . . Perez, E. (2023). Measuring faithfulness in chain-of-thought reasoning (arXiv:2307.13702). arXiv. https://doi.org/10.48550/arXiv.2307.13702
  4. Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019). Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications, 10(1), Article 1096. https://doi.org/10.1038/s41467-019-08987-4
  5. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  6. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551. https://doi.org/10.1162/neco.1989.1.4.541
  7. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
  8. LeCun, Y., Denker, J. S., & Solla, S. A. (1990). Optimal brain damage. In D. S. Touretzky (Ed.), Advances in Neural Information Processing Systems (Vol. 2, pp. 598–605). Morgan Kaufmann. https://proceedings.neurips.cc/paper_files/paper/1989/hash/6c9882bbac1c7093bd25041881277658-Abstract.html
  9. Lee, T. B. (2019, January 30). An AI crushed two human pros at StarCraft—but it wasn’t a fair fight. Ars Technica. https://arstechnica.com/gaming/2019/01/an-ai-crushed-two-human-pros-at-starcraft-but-it-wasnt-a-fair-fight/
  10. Legg, C. (2026). Peirce and generative AI. In R. Lane (Ed.), Pragmatism revisited (pp. 189–207). Cambridge University Press. https://doi.org/10.1017/9781009453776.013
  11. Leibniz, G. W. (1951). The art of discovery. In P. P. Wiener (Ed.), Leibniz selections (pp. 50–58). Charles Scribner's Sons. (Original work written 1685)
  12. Leivada, E., Marcus, G., Günther, F., & Murphy, E. (2026). A sentence is worth a thousand pictures: Can large language models understand hum4n L4ngu4ge and the W0rld behind W0rds? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 384(2320), Article 20250008. https://doi.org/10.1098/rsta.2025.0008
  13. LessWrong. (2022, November 30). Roko's basilisk. Retrieved September 30, 2026, from https://www.lesswrong.com/w/rokos-basilisk
  14. Levelt, W. J. M. (1989). Speaking: From intention to articulation. MIT Press. https://doi.org/10.7551/mitpress/6393.001.0001
  15. Levinstein, B. A., & Soares, N. (2020). Cheating death in Damascus. The Journal of Philosophy, 117(5), 237–266. https://doi.org/10.5840/jphil2020117516
  16. Levy, O., & Goldberg, Y. (2014). Neural word embedding as implicit matrix factorization. In Advances in Neural Information Processing Systems (Vol. 27). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2014/hash/b78666971ceae55a8e87efb7cbfd9ad4-Abstract.html
  17. Lewis, D. (1976). Probabilities of conditionals and conditional probabilities. The Philosophical Review, 85(3), 297–315. https://doi.org/10.2307/2184045
  18. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. In Advances in Neural Information Processing Systems (Vol. 33, pp. 9459–9474). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html
  19. Li, K., Hopkins, A. K., Bau, D., Viégas, F., Pfister, H., & Wattenberg, M. (2023). Emergent world representations: Exploring a sequence model trained on a synthetic task. In 11th International Conference on Learning Representations (ICLR 2023). https://openreview.net/forum?id=DeG07_TcZvT
  20. Lieber, O., Lenz, B., Bata, H., Cohen, G., Osin, J., Dalmedigos, I., Safahi, E., Meirom, S., Belinkov, Y., Shalev-Shwartz, S., Abend, O., Alon, R., Asida, T., Bergman, A., Glozman, R., Gokhman, M., Manevich, A., Ratner, N., Rozen, N., . . . Shoham, Y. (2024). Jamba: A hybrid Transformer-Mamba language model (arXiv:2403.19887). arXiv. https://doi.org/10.48550/arXiv.2403.19887
  21. Lindsay, G. W. (2020). Attention in psychology, neuroscience, and machine learning. Frontiers in Computational Neuroscience, 14, Article 29. https://doi.org/10.3389/fncom.2020.00029
  22. Lindsey, J. (2026). Emergent introspective awareness in large language models (arXiv:2601.01828). arXiv. https://doi.org/10.48550/arXiv.2601.01828
  23. Lindsey, J., Gurnee, W., Ameisen, E., Chen, B., Pearce, A., Turner, N. L., Citro, C., Abrahams, D., Carter, S., Hosmer, B., Marcus, J., Sklar, M., Templeton, A., Bricken, T., McDougall, C., Cunningham, H., Henighan, T., Jermyn, A., Jones, A., . . . Batson, J. (2025). On the biology of a large language model. Transformer Circuits Thread. https://transformer-circuits.pub/2025/attribution-graphs/biology.html
  24. Linzen, T. (2016). Issues in evaluating semantic spaces using word analogies. In Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP (pp. 13–18). Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-2503
  25. Linzen, T., Dupoux, E., & Goldberg, Y. (2016). Assessing the ability of LSTMs to learn syntax-sensitive dependencies. Transactions of the Association for Computational Linguistics, 4, 521–535. https://doi.org/10.1162/tacl_a_00115
  26. Lipton, Z. C. (2018). The mythos of model interpretability. Queue, 16(3), 31–57. https://doi.org/10.1145/3236386.3241340
  27. Liu, J., Zhao, X., Shang, X., & Shen, Z. (2026). Dive into Claude Code: The design space of today's and future AI agent systems (arXiv:2604.14228). arXiv. https://doi.org/10.48550/arXiv.2604.14228
  28. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach (arXiv:1907.11692). arXiv. https://doi.org/10.48550/arXiv.1907.11692
  29. Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. https://doi.org/10.1109/TIT.1982.1056489
  30. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., & Watkins, C. (2002). Text classification using string kernels. Journal of Machine Learning Research, 2, 419–444. https://www.jmlr.org/papers/v2/lodhi02a.html
  31. Lou, A., Meng, C., & Ermon, S. (2024). Discrete diffusion modeling by estimating the ratios of the data distribution. In R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, & F. Berkenkamp (Eds.), Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 32819–32848). PMLR. https://proceedings.mlr.press/v235/lou24a.html
  32. Lovecraft, H. P. (1936). At the mountains of madness [Serialized novella]. Astounding Stories, 16(6); 17(1); 17(2).
  33. Lu, C., Gallagher, J., Michala, J., Fish, K., & Lindsey, J. (2026). The Assistant Axis: Situating and stabilizing the default persona of language models (arXiv:2601.10387). arXiv. https://doi.org/10.48550/arXiv.2601.10387
  34. Luo, Y., Yang, Z., Meng, F., Li, Y., Zhou, J., & Zhang, Y. (2023). An empirical study of catastrophic forgetting in large language models during continual fine-tuning (arXiv:2308.08747). arXiv. https://doi.org/10.48550/arXiv.2308.08747
  35. Lynch, A., Wright, B., Larson, C., Ritchie, S. J., Mindermann, S., Hubinger, E., Perez, E., & Troy, K. (2025). Agentic misalignment: How LLMs could be insider threats (arXiv:2510.05179). arXiv. https://doi.org/10.48550/arXiv.2510.05179

M

  1. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. Le Cam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297). University of California Press.
  2. Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2024). Dissociating language and thought in large language models. Trends in Cognitive Sciences, 28(6), 517–540. https://doi.org/10.1016/j.tics.2024.01.011
  3. Mandelkern, M., & Linzen, T. (2024). Do language models’ words refer? Computational Linguistics, 50(3), 1191–1200. https://doi.org/10.1162/coli_a_00522
  4. Manheim, D. (2026). Language models’ hall of mirrors problem: Why AI alignment requires Peircean semiosis. Philosophy & Technology, 39(1), Article 9. https://doi.org/10.1007/s13347-025-00975-5
  5. Manheim, D., & Garrabrant, S. (2018). Categorizing variants of Goodhart's law (arXiv:1803.04585). arXiv. https://doi.org/10.48550/arXiv.1803.04585
  6. Markov, A. A. (2006). An example of statistical investigation of the text Eugene Onegin concerning the connection of samples in chains (G. Custance & D. Link, Trans.). Science in Context, 19(4), 591–600. https://doi.org/10.1017/S0269889706001074 (Original work published 1913)
  7. Marks, S., & Tegmark, M. (2024). The geometry of truth: Emergent linear structure in large language model representations of true/false datasets. In First Conference on Language Modeling (COLM 2024). https://openreview.net/forum?id=aajyHYjjsk
  8. Maron, M. E. (1961). Automatic indexing: An experimental inquiry. Journal of the ACM, 8(3), 404–417. https://doi.org/10.1145/321075.321084
  9. Marone, M., Weller, O., Fleshman, W., Yang, E., Lawrie, D., & Van Durme, B. (2025). mmBERT: A modern multilingual encoder with annealed language learning (arXiv:2509.06888). arXiv. https://doi.org/10.48550/arXiv.2509.06888
  10. Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.
  11. McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3), 419–457. https://doi.org/10.1037/0033-295X.102.3.419
  12. McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. Psychology of Learning and Motivation, 24, 109–165. https://doi.org/10.1016/S0079-7421(08)60536-8
  13. McCoy, R. T., Pavlick, E., & Linzen, T. (2019). Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In A. Korhonen, D. Traum, & L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3428–3448). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1334
  14. McCoy, R. T., Soulos, P., Linzen, T., & Smolensky, P. (2026). The emergent symbolic structure of artificial neural networks (arXiv:2608.29530). arXiv. https://doi.org/10.48550/arXiv.2608.29530
  15. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259
  16. McDermott, J. (1982). R1: A rule-based configurer of computer systems. Artificial Intelligence, 19(1), 39–88. https://doi.org/10.1016/0004-3702(82)90021-2
  17. McGrath, T., Rahtz, M., Kramar, J., Mikulik, V., & Legg, S. (2023). The Hydra effect: Emergent self-repair in language model computations (arXiv:2307.15771). arXiv. https://doi.org/10.48550/arXiv.2307.15771
  18. Medawar, P. B. (1961). Critical notice [Review of the book The phenomenon of man, by P. Teilhard de Chardin]. Mind, 70(277), 99–106. https://doi.org/10.1093/mind/LXX.277.99
  19. Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85(3), 207–238. https://doi.org/10.1037/0033-295X.85.3.207
  20. Meng, K., Bau, D., Andonian, A., & Belinkov, Y. (2022). Locating and editing factual associations in GPT. In Advances in Neural Information Processing Systems (Vol. 35, pp. 17359–17372). Curran Associates. https://doi.org/10.52202/068431-1262
  21. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space (arXiv:1301.3781). arXiv. https://doi.org/10.48550/arXiv.1301.3781
  22. Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010). Recurrent neural network based language model. In Interspeech 2010 (pp. 1045–1048). International Speech Communication Association. https://doi.org/10.21437/Interspeech.2010-343
  23. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (Vol. 26). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html
  24. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
  25. Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141–144. https://doi.org/10.1016/S1364-6613(03)00029-9
  26. Millière, R. (2025). Normative conflicts and shallow AI alignment. Philosophical Studies, 182(7), 2035–2078. https://doi.org/10.1007/s11098-025-02347-3
  27. Millière, R., & Buckner, C. (2024a). A philosophical introduction to language models—Part I: Continuity with classic debates (arXiv:2401.03910). arXiv. https://doi.org/10.48550/arXiv.2401.03910
  28. Millière, R., & Buckner, C. (2024b). A philosophical introduction to language models—Part II: The way forward (arXiv:2405.03207). arXiv. https://doi.org/10.48550/arXiv.2405.03207
  29. MiniMax. (2025). MiniMax-01: Scaling foundation models with lightning attention (arXiv:2501.08313). arXiv. https://doi.org/10.48550/arXiv.2501.08313
  30. Minsky, M., & Papert, S. (1969). Perceptrons: An introduction to computational geometry. MIT Press.
  31. Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Pelican Books.
  32. Mitchell, M., & Krakauer, D. C. (2023). The debate over understanding in AI’s large language models. Proceedings of the National Academy of Sciences, 120(13), Article e2215907120. https://doi.org/10.1073/pnas.2215907120
  33. Mitchell, T. M. (1980). The need for biases in learning generalizations (Technical Report CBM-TR-117). Department of Computer Science, Rutgers University. https://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf
  34. Mollick, E. (2026). Built for something else: Sources and notes [Sources companion to the video "Built for something else: How a chain of accidents built ChatGPT"; written by Claude Opus 5.5, supervised by E. Mollick]. Retrieved September 30, 2026, from https://built-for-something-else.netlify.app/
  35. Morris, M. R., Sohl-Dickstein, J., Fiedel, N., Warkentin, T., Dafoe, A., Faust, A., Farabet, C., & Legg, S. (2024). Position: Levels of AGI for operationalizing progress on the path to AGI. In R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, & F. Berkenkamp (Eds.), Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 36308–36321). PMLR. https://proceedings.mlr.press/v235/morris24b.html
  36. Muennighoff, N., Rush, A. M., Barak, B., Le Scao, T., Piktus, A., Tazi, N., Pyysalo, S., Wolf, T., & Raffel, C. (2025). Scaling data-constrained language models. Journal of Machine Learning Research, 26(53), 1–66. https://jmlr.org/papers/v26/24-1000.html

N

  1. Nanda, N., Lee, A., & Wattenberg, M. (2023). Emergent linear representations in world models of self-supervised sequence models. In Y. Belinkov, S. Hao, J. Jumelet, N. Kim, A. McCarthy, & H. Mohebbi (Eds.), Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP (pp. 16–30). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.blackboxnlp-1.2
  2. Narayanan, A., & Kapoor, S. (2024, July 26). AI existential risk probabilities are too unreliable to inform policy. AI Snake Oil. https://www.normaltech.ai/p/ai-existential-risk-probabilities
  3. Narayanan, A., & Kapoor, S. (2025, April 15). AI as normal technology: An alternative to the vision of AI as a potential superintelligence. Knight First Amendment Institute at Columbia University. https://knightcolumbia.org/content/ai-as-normal-technology
  4. Nasr, M., Rando, J., Carlini, N., Hayase, J., Jagielski, M., Cooper, A. F., Ippolito, D., Choquette-Choo, C. A., Tramèr, F., & Lee, K. (2025). Scalable extraction of training data from aligned, production language models. In 13th International Conference on Learning Representations (ICLR 2025) (pp. 82363–82435). https://proceedings.iclr.cc/paper_files/paper/2025/hash/cce0e917b050208170151f77b497fc71-Abstract-Conference.html
  5. National Institute of Standards and Technology. (2025, September 30). CAISI evaluation of DeepSeek AI models finds shortcomings and risks. https://www.nist.gov/news-events/news/2025/09/caisi-evaluation-deepseek-ai-models-finds-shortcomings-and-risks
  6. Nellis, S., & Lee, J. (2023, March 22). Nvidia tweaks flagship H100 chip for export to China as H800. Reuters. https://www.reuters.com/technology/nvidia-tweaks-flagship-h100-chip-export-china-h800-2023-03-21/
  7. Newell, A., & Simon, H. A. (1956). The logic theory machine: A complex information processing system. IRE Transactions on Information Theory, 2(3), 61–79. https://doi.org/10.1109/TIT.1956.1056797
  8. Newell, A., & Simon, H. A. (1972). Human problem solving. Prentice-Hall.
  9. Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126. https://doi.org/10.1145/360018.360022
  10. Newell, A., Shaw, J. C., & Simon, H. A. (1957). Empirical explorations of the logic theory machine: A case study in heuristic. In Papers presented at the February 26–28, 1957, Western Joint Computer Conference: Techniques for reliability (pp. 218–230). Association for Computing Machinery. https://doi.org/10.1145/1455567.1455605
  11. Nie, S., Zhu, F., You, Z., Zhang, X., Ou, J., Hu, J., Zhou, J., Lin, Y., Wen, J.-R., & Li, C. (2025). Large language diffusion models. In Advances in Neural Information Processing Systems (Vol. 38, pp. 56354–56392). Curran Associates. https://doi.org/10.52202/085713-1689
  12. Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020). Adversarial NLI: A new benchmark for natural language understanding. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4885–4901). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.441
  13. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3), 231–259. https://doi.org/10.1037/0033-295X.84.3.231
  14. Nissim, M., van Noord, R., & van der Goot, R. (2020). Fair is better than sensational: Man is to doctor as woman is to doctor. Computational Linguistics, 46(2), 487–497. https://doi.org/10.1162/coli_a_00379
  15. Niven, T., & Kao, H.-Y. (2019). Probing neural network comprehension of natural language arguments. In A. Korhonen, D. Traum, & L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4658–4664). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1459
  16. Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115(1), 39–57. https://doi.org/10.1037/0096-3445.115.1.39
  17. Nozick, R. (1969). Newcomb’s problem and two principles of choice. In N. Rescher (Ed.), Essays in honor of Carl G. Hempel (pp. 114–146). D. Reidel. https://doi.org/10.1007/978-94-017-1466-2_7
  18. NVIDIA. (2025a). Nemotron-H: A family of accurate and efficient hybrid Mamba-Transformer models (arXiv:2504.03624). arXiv. https://doi.org/10.48550/arXiv.2504.03624
  19. NVIDIA. (2025b). NVIDIA Nemotron 3: Efficient and open intelligence (arXiv:2512.20856). arXiv. https://doi.org/10.48550/arXiv.2512.20856

O

  1. O'Donoghue, B., & Flennerhag, S. (2026, June 10). DiffusionGemma: 4x faster text generation. The Keyword. https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/
  2. Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., & Carter, S. (2020). Zoom in: An introduction to circuits. Distill, 5(3), e00024.001. https://doi.org/10.23915/distill.00024.001
  3. Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583), 607–609. https://doi.org/10.1038/381607a0
  4. Olsson, C., Elhage, N., Nanda, N., Joseph, N., DasSarma, N., Henighan, T., Mann, B., Askell, A., Bai, Y., Chen, A., Conerly, T., Drain, D., Ganguli, D., Hatfield-Dodds, Z., Hernandez, D., Johnston, S., Jones, A., Kernion, J., Lovitt, L., . . . Olah, C. (2022). In-context learning and induction heads. Transformer Circuits Thread. https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html
  5. Omohundro, S. M. (2008). The basic AI drives. In P. Wang, B. Goertzel, & S. Franklin (Eds.), Artificial general intelligence 2008: Proceedings of the First AGI Conference (pp. 483–492). IOS Press.
  6. Open Source Initiative. (2024, October 28). The Open Source AI Definition – 1.0. https://opensource.org/ai/open-source-ai-definition
  7. OpenAI. (2022, November 30). Introducing ChatGPT. https://openai.com/index/chatgpt/
  8. OpenAI. (2023). GPT-4 technical report (arXiv:2303.08774). arXiv. https://doi.org/10.48550/arXiv.2303.08774
  9. OpenAI. (2024, September 12). Learning to reason with LLMs. https://openai.com/index/learning-to-reason-with-llms/
  10. OpenAI. (2025, April 29). Sycophancy in GPT-4o: What happened and what we’re doing about it. https://openai.com/index/sycophancy-in-gpt-4o/
  11. OpenAI. (n.d.). Model card: Whisper. GitHub. Retrieved October 1, 2026, from https://github.com/openai/whisper/blob/main/model-card.md
  12. Ord, T. (2020). The precipice: Existential risk and the future of humanity. Hachette Books.
  13. osmarks. (n.d.). P(Doom) song objectively correct interpretation. GTech™ Documentation. Retrieved September 30, 2026, from https://docs.osmarks.net/hypha/p(doom)_song_objectively_correct_interpretation
  14. Østergaard, S. D., & Nielbo, K. L. (2023). False responses from artificial intelligence models are not hallucinations. Schizophrenia Bulletin, 49(5), 1105–1107. https://doi.org/10.1093/schbul/sbad068
  15. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems (Vol. 35, pp. 27730–27744). Curran Associates. https://doi.org/10.52202/068431-2011

P

  1. Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A method for automatic evaluation of machine translation. In P. Isabelle, E. Charniak, & D. Lin (Eds.), Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (pp. 311–318). Association for Computational Linguistics. https://doi.org/10.3115/1073083.1073135
  2. Park, J. S., O’Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (pp. 1–22). Association for Computing Machinery. https://doi.org/10.1145/3586183.3606763
  3. Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global vectors for word representation. In A. Moschitti, B. Pang, & W. Daelemans (Eds.), Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1532–1543). Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1162
  4. Pereira, F. (2000). Formal grammar and information theory: Together again? Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 358(1769), 1239–1253. https://doi.org/10.1098/rsta.2000.0583
  5. Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., Jones, A., Chen, A., Mann, B., Israel, B., Seethor, B., McKinnon, C., Olah, C., Yan, D., Amodei, D., . . . Kaplan, J. (2023). Discovering language model behaviors with model-written evaluations. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Findings of the Association for Computational Linguistics: ACL 2023 (pp. 13387–13434). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-acl.847
  6. Perrigo, B. (2023, January 18). Exclusive: OpenAI used Kenyan workers on less than $2 per hour to make ChatGPT less toxic. TIME. https://time.com/6247678/openai-chatgpt-kenya-workers/
  7. Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 2227–2237). Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-1202
  8. Petrov, A., La Malfa, E., Torr, P., & Bibi, A. (2023). Language model tokenizers introduce unfairness between languages. In Advances in Neural Information Processing Systems (Vol. 36, pp. 36963–36990). Curran Associates. https://doi.org/10.52202/075280-1608
  9. Pfungst, O. (1911). Clever Hans (the horse of Mr. von Osten): A contribution to experimental animal and human psychology (C. L. Rahn, Trans.). Henry Holt and Company. https://doi.org/10.5962/bhl.title.56164 (Original work published 1907)
  10. Piantadosi, S. T. (2024). Modern language models refute Chomsky’s approach to language. In E. Gibson & M. Poliak (Eds.), From fieldwork to linguistic theory: A tribute to Dan Everett (pp. 353–414). Language Science Press. https://doi.org/10.5281/zenodo.12665933
  11. Piantadosi, S. T., & Hill, F. (2022). Meaning without reference in large language models (arXiv:2208.02957). arXiv. https://doi.org/10.48550/arXiv.2208.02957
  12. Pichai, S., & Hassabis, D. (2023, December 6). Introducing Gemini: Our largest and most capable AI model. The Keyword. https://blog.google/technology/ai/google-gemini-ai/
  13. Pope, R., Douglas, S., Chowdhery, A., Devlin, J., Bradbury, J., Levskaya, A., Heek, J., Xiao, K., Agrawal, S., & Dean, J. (2023). Efficiently scaling transformer inference. In Proceedings of Machine Learning and Systems (Vol. 5, pp. 606–624). https://proceedings.mlsys.org/paper_files/paper/2023/hash/c4be71ab8d24cdfb45e3d06dbfca2780-Abstract-mlsys2023.html
  14. Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77(3, Pt. 1), 353–363. https://doi.org/10.1037/h0025953
  15. Post, E. L. (1943). Formal reductions of the general combinatorial decision problem. American Journal of Mathematics, 65(2), 197–215. https://doi.org/10.2307/2371809
  16. Power, A., Burda, Y., Edwards, H., Babuschkin, I., & Misra, V. (2022). Grokking: Generalization beyond overfitting on small algorithmic datasets (arXiv:2201.02177). arXiv. https://doi.org/10.48550/arXiv.2201.02177
  17. Pressman, J. D. (2023, October 15). Hermes lecture #3: Why do cognitive scientists hate LLMs? minihf.com. https://minihf.com/posts/2023-10-16-hermes-lecture-3-why-do-cognitive-scientists-hate-llms/
  18. Putnam, H. (1967). Psychological predicates. In W. H. Capitan & D. D. Merrill (Eds.), Art, mind, and religion (pp. 37–48). University of Pittsburgh Press. https://doi.org/10.2307/jj.6380610.6

Q

  1. Quilty-Dunn, J., Porot, N., & Mandelbaum, E. (2023). The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences. Behavioral and Brain Sciences, 46, Article e261. https://doi.org/10.1017/S0140525X22002849
  2. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1007/BF00116251
  3. Qwen Team. (2025). Qwen3-Next-80B-A3B-Instruct [Model card]. Hugging Face. https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct
  4. Qwen Team. (2026). Qwen3.5-397B-A17B [Model card]. Hugging Face. https://huggingface.co/Qwen/Qwen3.5-397B-A17B

R

  1. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (pp. 8748–8763). PMLR. https://proceedings.mlr.press/v139/radford21a.html
  2. Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2023). Robust speech recognition via large-scale weak supervision. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 28492–28518). PMLR. https://proceedings.mlr.press/v202/radford23a.html
  3. Radford, A., Metz, L., & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. In 4th International Conference on Learning Representations (ICLR 2016). https://doi.org/10.48550/arXiv.1511.06434
  4. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training [Technical report]. OpenAI. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
  5. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners [Technical report]. OpenAI. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
  6. Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023). Direct preference optimization: Your language model is secretly a reward model. In Advances in Neural Information Processing Systems (Vol. 36, pp. 53728–53741). Curran Associates. https://doi.org/10.52202/075280-2338
  7. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67. https://jmlr.org/papers/v21/20-074.html
  8. Raghavan, P. (2024, February 23). Gemini image generation got it wrong. We'll do better. The Keyword. https://blog.google/products/gemini/gemini-image-generation-issue/
  9. Raji, I. D., Bender, E. M., Paullada, A., Denton, E., & Hanna, A. (2021). AI and the everything in the whole wide world benchmark. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (Vol. 1). https://doi.org/10.48550/arXiv.2111.15366
  10. Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Adler, T., Kreil, D., Kopp, M. K., Klambauer, G., Brandstetter, J., & Hochreiter, S. (2021). Hopfield networks is all you need. In 9th International Conference on Learning Representations (ICLR 2021). https://openreview.net/forum?id=tL89RnzIiCd
  11. Ratcliff, R. (1990). Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Psychological Review, 97(2), 285–308. https://doi.org/10.1037/0033-295X.97.2.285
  12. Reed, S., Zolna, K., Parisotto, E., Gómez Colmenarejo, S., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., & de Freitas, N. (2022). A generalist agent. Transactions on Machine Learning Research. https://openreview.net/forum?id=1ikK0kHjvj
  13. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act). (2024). Official Journal of the European Union, L 2024/1689. http://data.europa.eu/eli/reg/2024/1689/oj
  14. Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3982–3992). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410
  15. Reiter, E. (2018). A structured review of the validity of BLEU. Computational Linguistics, 44(3), 393–401. https://doi.org/10.1162/coli_a_00322
  16. Rescorla, R. A. (1968). Probability of shock in the presence and absence of CS in fear conditioning. Journal of Comparative and Physiological Psychology, 66(1), 1–5. https://doi.org/10.1037/h0025984
  17. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 64–99). Appleton-Century-Crofts.
  18. Reynolds, L., & McDonell, K. (2021). Multiversal views on language models. In D. Glowacka & V. Krishnamurthy (Eds.), Joint Proceedings of the ACM IUI 2021 Workshops (CEUR Workshop Proceedings, Vol. 2903). CEUR-WS.org. https://ceur-ws.org/Vol-2903/IUI21WS-HAIGEN-11.pdf
  19. Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. In E. P. Xing & T. Jebara (Eds.), Proceedings of the 31st International Conference on Machine Learning (Vol. 32, pp. 1278–1286). PMLR. https://proceedings.mlr.press/v32/rezende14.html
  20. Rini, R. (2020). Deepfakes and the epistemic backstop. Philosophers’ Imprint, 20(24), 1–16. http://hdl.handle.net/2027/spo.3521354.0020.024
  21. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 10674–10685). IEEE. https://doi.org/10.1109/CVPR52688.2022.01042
  22. Roose, K. (2023a, February 16). A conversation with Bing’s chatbot left me deeply unsettled. The New York Times. https://www.nytimes.com/2023/02/16/technology/bing-chatbot-microsoft-chatgpt.html
  23. Roose, K. (2023b, May 30). Why an octopus-like creature has come to symbolize the state of A.I. The New York Times. https://www.nytimes.com/2023/05/30/technology/shoggoth-meme-ai.html
  24. Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology: General, 104(3), 192–233. https://doi.org/10.1037/0096-3445.104.3.192
  25. Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7(4), 573–605. https://doi.org/10.1016/0010-0285(75)90024-9
  26. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386–408. https://doi.org/10.1037/h0042519
  27. Ross, S., Gordon, G. J., & Bagnell, J. A. (2011). A reduction of imitation learning and structured prediction to no-regret online learning. In G. Gordon, D. Dunson, & M. Dudík (Eds.), Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (pp. 627–635). PMLR. https://proceedings.mlr.press/v15/ross11a.html
  28. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
  29. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0
  30. Russin, J., McGrath, S. W., & Williams, D. J. (2025). From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks (arXiv:2405.15164). arXiv. https://doi.org/10.48550/arXiv.2405.15164

S

  1. Sahoo, S. S., Arriola, M., Schiff, Y., Gokaslan, A., Marroquin, E., Chiu, J. T., Rush, A., & Kuleshov, V. (2024). Simple and effective masked diffusion language models. In Advances in Neural Information Processing Systems (Vol. 37, pp. 130136–130184). Curran Associates. https://doi.org/10.52202/079017-4135
  2. Sainz, O., Campos, J., García-Ferrero, I., Etxaniz, J., Lopez de Lacalle, O., & Agirre, E. (2023). NLP evaluation in trouble: On the need to measure LLM data contamination for each benchmark. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 10776–10787). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.722
  3. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter (arXiv:1910.01108). arXiv. https://doi.org/10.48550/arXiv.1910.01108
  4. Santelli, M. (2026, August 25). El futuro de la IA: Entre doomers y hyperos. 421.news. https://www.421.news/es/ia-doomers-hyperos/
  5. Santelli, M., Toranzo Calderón, J., Caso, R., & Muntaabski, B. (2026). ESPANLI: Un benchmark adversarial de NLI para el español rioplatense mediante competencias humano–modelo [ESPANLI: An adversarial NLI benchmark for Río de la Plata Spanish via human–model competitions]. In Memorias del Simposio Argentino de Inteligencia Artificial y Ciencia de Datos (ASAID), 55 Jornadas Argentinas de Informática (JAIIO). SADIO. https://55jaiio.sadio.org.ar/wp-content/uploads/2026/07/395.pdf
  6. Saussure, F. de. (1971). Cours de linguistique générale [Course in general linguistics] (C. Bally & A. Sechehaye, Eds.). Payot. (Original work published 1916)
  7. Schaeffer, R., Miranda, B., & Koyejo, S. (2023). Are emergent abilities of large language models a mirage? In Advances in Neural Information Processing Systems (Vol. 36, pp. 55565–55581). Curran Associates. https://doi.org/10.52202/075280-2425
  8. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. In Advances in Neural Information Processing Systems (Vol. 36, pp. 68539–68551). Curran Associates. https://doi.org/10.52202/075280-2997
  9. Schlag, I., Irie, K., & Schmidhuber, J. (2021). Linear transformers are secretly fast weight programmers. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 9355–9366). PMLR. https://proceedings.mlr.press/v139/schlag21a.html
  10. Schlatter, J., Weinstein-Raun, B., & Ladish, J. (2026). Incomplete tasks induce shutdown resistance in some frontier LLMs. Transactions on Machine Learning Research. https://openreview.net/forum?id=e4bTTqUnJH
  11. Schmidhuber, J. (2003). Goedel machines: Self-referential universal problem solvers making provably optimal self-improvements (arXiv:cs/0309048). arXiv. https://doi.org/10.48550/arXiv.cs/0309048
  12. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms (arXiv:1707.06347). arXiv. https://doi.org/10.48550/arXiv.1707.06347
  13. Schwarz, W. (2018, December 27). On functional decision theory. wo’s weblog. https://www.umsu.de/blog/2018/688
  14. Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417–424. https://doi.org/10.1017/S0140525X00005756
  15. Searle, J. R. (1990). Is the brain’s mind a computer program? Scientific American, 262(1), 26–31. https://doi.org/10.1038/scientificamerican0190-26
  16. Sennrich, R., Haddow, B., & Birch, A. (2016). Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1715–1725). Association for Computational Linguistics. https://doi.org/10.18653/v1/P16-1162
  17. Shanahan, M. (2024). Talking about large language models. Communications of the ACM, 67(2), 68–79. https://doi.org/10.1145/3624724
  18. Shanahan, M., McDonell, K., & Reynolds, L. (2023). Role play with large language models. Nature, 623(7987), 493–498. https://doi.org/10.1038/s41586-023-06647-8
  19. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423; 27(4), 623–656. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
  20. Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., Durmus, E., Hatfield-Dodds, Z., Johnston, S. R., Kravec, S., Maxwell, T., McCandlish, S., Ndousse, K., Rausch, O., Schiefer, N., Yan, D., Zhang, M., & Perez, E. (2024). Towards understanding sycophancy in language models. In 12th International Conference on Learning Representations (ICLR 2024) (pp. 110–144). https://proceedings.iclr.cc/paper_files/paper/2024/hash/0105f7972202c1d4fb817da9f21a9663-Abstract-Conference.html
  21. Shazeer, N. (2019). Fast transformer decoding: One write-head is all you need (arXiv:1911.02150). arXiv. https://doi.org/10.48550/arXiv.1911.02150
  22. Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017). Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In 5th International Conference on Learning Representations (ICLR 2017). https://openreview.net/forum?id=B1ckMDqlg
  23. Shortliffe, E. H. (1976). Computer-based medical consultations: MYCIN. Elsevier. https://doi.org/10.1016/B978-0-444-00179-5.X5001-X
  24. Shrager, J. (2024). ELIZA reinterpreted: The world’s first chatbot was not intended as a chatbot at all (arXiv:2406.17650). arXiv. https://doi.org/10.48550/arXiv.2406.17650
  25. Shrager, J. (2026). Executable archaeology: Reanimating the Logic Theorist from its IPL-V source (arXiv:2603.13514). arXiv. https://doi.org/10.48550/arXiv.2603.13514
  26. Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755–759. https://doi.org/10.1038/s41586-024-07566-y
  27. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
  28. Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140–1144. https://doi.org/10.1126/science.aar6404
  29. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359. https://doi.org/10.1038/nature24270
  30. Singer, P. (1972). Famine, affluence, and morality. Philosophy & Public Affairs, 1(3), 229–243. https://www.jstor.org/stable/2265052
  31. Skalse, J., Howe, N. H. R., Krasheninnikov, D., & Krueger, D. (2022). Defining and characterizing reward hacking. In Advances in Neural Information Processing Systems (Vol. 35, pp. 9460–9471). Curran Associates. https://doi.org/10.52202/068431-0687
  32. Smith, A. L., Greaves, F., & Panch, T. (2023). Hallucination or confabulation? Neuroanatomy as metaphor in large language models. PLOS Digital Health, 2(11), Article e0000388. https://doi.org/10.1371/journal.pdig.0000388
  33. Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11(1), 1–23. https://doi.org/10.1017/S0140525X00052432
  34. Snell, J., Swersky, K., & Zemel, R. S. (2017). Prototypical networks for few-shot learning. In Advances in Neural Information Processing Systems (Vol. 30). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2017/hash/cb8da6767461f2812ae4290eac7cbc42-Abstract.html
  35. Soares, N. (2022, June 15). A central AI alignment problem: Capabilities generalization, and the sharp left turn. LessWrong. https://www.lesswrong.com/posts/GNhMPAWcfBCASy8e6/a-central-ai-alignment-problem-capabilities-generalization
  36. Soares, N., Fallenstein, B., Yudkowsky, E., & Armstrong, S. (2015). Corrigibility. In Artificial intelligence and ethics: Papers from the 2015 AAAI Workshop (AAAI Technical Report WS-15-02). AAAI Press. https://aaai.org/papers/aaaiw-ws0067-15-10124/
  37. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. In F. Bach & D. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning (Vol. 37, pp. 2256–2265). PMLR. https://proceedings.mlr.press/v37/sohl-dickstein15.html
  38. Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J. W., Kreps, S., McCain, M., Newhouse, A., Blazakis, J., McGuffie, K., & Wang, J. (2019). Release strategies and the social impacts of language models (arXiv:1908.09203). arXiv. https://doi.org/10.48550/arXiv.1908.09203
  39. Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10(1), 89–96. https://doi.org/10.1111/j.1467-7687.2007.00569.x
  40. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(56), 1929–1958. https://jmlr.org/papers/v15/srivastava14a.html
  41. Standage, T. (2002). The Turk: The life and times of the famous eighteenth-century chess-playing machine. Walker & Company.
  42. Strathern, M. (1997). ‘Improving ratings’: Audit in the British University system. European Review, 5(3), 305–321. https://doi.org/10.1002/(SICI)1234-981X(199707)5:3%3C305::AID-EURO184%3E3.0.CO;2-4
  43. Subin, S. (2025, January 27). Nvidia sheds almost $600 billion in market cap, biggest one-day loss in U.S. history. CNBC. https://www.cnbc.com/2025/01/27/nvidia-sheds-almost-600-billion-in-market-cap-biggest-drop-ever.html
  44. Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems (Vol. 27). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2014/hash/5a18e133cbf9f257297f410bb7eca942-Abstract.html
  45. Sutton, R. (2019, March 13). The bitter lesson. Incomplete Ideas. http://www.incompleteideas.net/IncIdeas/BitterLesson.html
  46. Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88(2), 135–170. https://doi.org/10.1037/0033-295X.88.2.135
  47. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
  48. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. In 2nd International Conference on Learning Representations (ICLR 2014). https://doi.org/10.48550/arXiv.1312.6199

T

  1. Tanzer, G., Suzgun, M., Visser, E., Jurafsky, D., & Melas-Kyriazi, L. (2024). A benchmark for learning to translate a new language from one grammar book. In 12th International Conference on Learning Representations (ICLR 2024) (pp. 18955–18985). https://proceedings.iclr.cc/paper_files/paper/2024/hash/52d63f9e4b81f866bf69fb3c834aad47-Abstract-Conference.html
  2. Taylor, W. L. (1953). “Cloze procedure”: A new tool for measuring readability. Journalism Quarterly, 30(4), 415–433. https://doi.org/10.1177/107769905303000401
  3. Teilhard de Chardin, P. (1959). The phenomenon of man (B. Wall, Trans.). Harper & Brothers. (Original work published 1955)
  4. Templeton, A., Conerly, T., Marcus, J., Lindsey, J., Bricken, T., Chen, B., Pearce, A., Citro, C., Ameisen, E., Jones, A., Cunningham, H., Turner, N. L., McDougall, C., MacDiarmid, M., Tamkin, A., Durmus, E., Hume, T., Mosconi, F., Freeman, C. D., . . . Henighan, T. (2024). Scaling monosemanticity: Extracting interpretable features from Claude 3 Sonnet. Transformer Circuits Thread. https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
  5. Tenney, I., Das, D., & Pavlick, E. (2019). BERT rediscovers the classical NLP pipeline. In A. Korhonen, D. Traum, & L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4593–4601). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1452
  6. Tesler, L. (n.d.). Adages and coinages. Retrieved September 30, 2026, from https://www.nomodes.com/larry-tesler-consulting/adages-and-coinages
  7. The AlphaStar team. (2019a, January 24). AlphaStar: Mastering the real-time strategy game StarCraft II. Google DeepMind. https://deepmind.google/discover/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii/
  8. The AlphaStar team. (2019b, October 30). AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning. Google DeepMind. https://deepmind.google/discover/blog/alphastar-grandmaster-level-in-starcraft-ii-using-multi-agent-reinforcement-learning/
  9. The Royal Swedish Academy of Sciences. (2024a, October 9). The Nobel Prize in Chemistry 2024 [Press release]. NobelPrize.org. https://www.nobelprize.org/prizes/chemistry/2024/press-release/
  10. The Royal Swedish Academy of Sciences. (2024b, October 8). The Nobel Prize in Physics 2024 [Press release]. NobelPrize.org. https://www.nobelprize.org/prizes/physics/2024/press-release/
  11. Tipler, F. J. (1994). The physics of immortality: Modern cosmology, God and the resurrection of the dead. Doubleday.
  12. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., & Lample, G. (2023). LLaMA: Open and efficient foundation language models (arXiv:2302.13971). arXiv. https://doi.org/10.48550/arXiv.2302.13971
  13. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Canton Ferrer, C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., . . . Scialom, T. (2023). Llama 2: Open foundation and fine-tuned chat models (arXiv:2307.09288). arXiv. https://doi.org/10.48550/arXiv.2307.09288
  14. Tsai, Y.-H. H., Bai, S., Yamada, M., Morency, L.-P., & Salakhutdinov, R. (2019). Transformer dissection: An unified understanding for transformer’s attention via the lens of kernel. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 4344–4353). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1443
  15. Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, s2-42(1), 230–265. https://doi.org/10.1112/plms/s2-42.1.230
  16. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
  17. Turner, A. M., Smith, L., Shah, R., Critch, A., & Tadepalli, P. (2021). Optimal policies tend to seek power. In Advances in Neural Information Processing Systems (Vol. 34, pp. 23063–23074). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2021/hash/c26820b8a4c1b3c2aa868d6d57e14a79-Abstract.html
  18. Turner, A. M., Thiergart, L., Udell, D., Leech, G., Mini, U., & MacDiarmid, M. (2023). Activation addition: Steering language models without optimization (arXiv:2308.10248v3). arXiv. https://doi.org/10.48550/arXiv.2308.10248v3
  19. Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023). Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting. In Advances in Neural Information Processing Systems (Vol. 36, pp. 74952–74965). Curran Associates. https://doi.org/10.52202/075280-3275
  20. Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327–352. https://doi.org/10.1037/0033-295X.84.4.327

U

  1. Ulam, S. (1958). John von Neumann 1903–1957. Bulletin of the American Mathematical Society, 64(3, Pt. 2), 1–49. https://doi.org/10.1090/S0002-9904-1958-10189-5

V

  1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
  2. Vig, J., Gehrmann, S., Belinkov, Y., Qian, S., Nevo, D., Singer, Y., & Shieber, S. (2020). Investigating gender bias in language models using causal mediation analysis. In Advances in Neural Information Processing Systems (Vol. 33, pp. 12388–12401). Curran Associates. https://proceedings.neurips.cc/paper_files/paper/2020/hash/92650b2e92217715fe312e6fa7b90d82-Abstract.html
  3. Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning (pp. 1096–1103). Association for Computing Machinery. https://doi.org/10.1145/1390156.1390294
  4. Vinge, V. (1993). The coming technological singularity: How to survive in the post-human era. In Vision-21: Interdisciplinary science and engineering in the era of cyberspace (NASA Conference Publication 10129, pp. 11–22). NASA Lewis Research Center. https://ntrs.nasa.gov/citations/19940022856
  5. Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Oh, J., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J. P., Jaderberg, M., … Silver, D. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354. https://doi.org/10.1038/s41586-019-1724-z
  6. Vinyals, O., Ewalds, T., Bartunov, S., Georgiev, P., Vezhnevets, A. S., Yeo, M., Makhzani, A., Küttler, H., Agapiou, J., Schrittwieser, J., Quan, J., Gaffney, S., Petersen, S., Simonyan, K., Schaul, T., van Hasselt, H., Silver, D., Lillicrap, T., Calderone, K., … Tsing, R. (2017). StarCraft II: A new challenge for reinforcement learning (arXiv:1708.04782). arXiv. https://doi.org/10.48550/arXiv.1708.04782
  7. Voita, E., Talbot, D., Moiseev, F., Sennrich, R., & Titov, I. (2019). Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. In A. Korhonen, D. Traum, & L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 5797–5808). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1580

W

  1. Waleffe, R., Byeon, W., Riach, D., Norick, B., Korthikanti, V., Dao, T., Gu, A., Hatamizadeh, A., Singh, S., Narayanan, D., Kulshreshtha, G., Singh, V., Casper, J., Kautz, J., Shoeybi, M., & Catanzaro, B. (2024). An empirical study of Mamba-based language models (arXiv:2406.07887). arXiv. https://doi.org/10.48550/arXiv.2406.07887
  2. Wallace, E., Xiao, K., Leike, R., Weng, L., Heidecke, J., & Beutel, A. (2024). The instruction hierarchy: Training LLMs to prioritize privileged instructions (arXiv:2404.13208). arXiv. https://doi.org/10.48550/arXiv.2404.13208
  3. Wang, K., Variengien, A., Conmy, A., Shlegeris, B., & Steinhardt, J. (2023). Interpretability in the wild: A circuit for indirect object identification in GPT-2 small. In 11th International Conference on Learning Representations (ICLR 2023). https://openreview.net/forum?id=NpsVSN6o4ul
  4. Warner, B., Chaffin, A., Clavié, B., Weller, O., Hallström, O., Taghadouini, S., Gallagher, A., Biswas, R., Ladhak, F., Aarsen, T., Adams, G. T., Howard, J., & Poli, I. (2025). Smarter, better, faster, longer: A modern bidirectional encoder for fast, memory efficient, and long context finetuning and inference. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2526–2547). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.acl-long.127
  5. Warren, T. (2023, February 14). These are Microsoft’s Bing AI secret rules and why it says it’s named Sydney. The Verge. https://www.theverge.com/23599441/microsoft-bing-ai-sydney-secret-rules
  6. Weaver, W. (1949, July 15). Translation [Memorandum]. The Rockefeller Foundation. https://www.mt-archive.net/50/Weaver-1949.pdf
  7. Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2022). Finetuned language models are zero-shot learners. In 10th International Conference on Learning Representations (ICLR 2022). https://openreview.net/forum?id=gEZrGCozdqR
  8. Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., & Fedus, W. (2022). Emergent abilities of large language models. Transactions on Machine Learning Research. https://openreview.net/forum?id=yzkSU5zdwD
  9. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems (Vol. 35, pp. 24824–24837). Curran Associates. https://doi.org/10.52202/068431-1800
  10. Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168
  11. Weizenbaum, J. (1976). Computer power and human reason: From judgment to calculation. W. H. Freeman.
  12. Weller, O., Ricci, K., Marone, M., Chaffin, A., Lawrie, D., & Van Durme, B. (2025). Seq vs seq: An open suite of paired encoders and decoders (arXiv:2507.11412). arXiv. https://doi.org/10.48550/arXiv.2507.11412
  13. Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits (Technical Report No. 1553-1). Stanford Electronics Laboratories, Stanford University.
  14. Wiegreffe, S., & Pinter, Y. (2019). Attention is not not explanation. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 11–20). Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1002
  15. Wiener, N. (1960). Some moral and technical consequences of automation. Science, 131(3410), 1355–1358. https://doi.org/10.1126/science.131.3410.1355
  16. Williams, A., Nangia, N., & Bowman, S. (2018). A broad-coverage challenge corpus for sentence understanding through inference. In M. Walker, H. Ji, & A. Stent (Eds.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 1112–1122). Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-1101
  17. Willison, S. (2022, September 12). Prompt injection attacks against GPT-3. Simon Willison’s Weblog. https://simonwillison.net/2022/Sep/12/prompt-injection/
  18. Willison, S. (2024, May 8). Slop is the new name for unwanted AI-generated content. Simon Willison’s Weblog. https://simonwillison.net/2024/May/8/slop/
  19. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data mining: Practical machine learning tools and techniques (4th ed.). Morgan Kaufmann. https://doi.org/10.1016/C2015-0-02071-8
  20. Wittgenstein, L. (2009). Philosophical investigations (G. E. M. Anscombe, P. M. S. Hacker, & J. Schulte, Trans.; P. M. S. Hacker & J. Schulte, Eds.; Rev. 4th ed.). Wiley-Blackwell. (Original work published 1953)
  21. Woydt, T., Willig, M., Wüst, A., Helff, L., Stammer, W., Rothkopf, C. A., & Kersting, K. (2026). Fodor and Pylyshyn’s legacy: Still no human-like systematic compositionality in neural networks (arXiv:2506.01820). arXiv. https://doi.org/10.48550/arXiv.2506.01820
  22. Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, X., Kaiser, Ł., Gouws, S., Kato, Y., Kudo, T., Kazawa, H., . . . Dean, J. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation (arXiv:1609.08144). arXiv. https://doi.org/10.48550/arXiv.1609.08144

Y

  1. Yanaka, H., Mineshima, K., Bekki, D., Inui, K., Sekine, S., Abzianidze, L., & Bos, J. (2019). Can neural networks understand monotonicity reasoning? In T. Linzen, G. Chrupała, Y. Belinkov, & D. Hupkes (Eds.), Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (pp. 31–40). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4804
  2. Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Yin, B., & Hu, X. (2023). Harnessing the power of LLMs in practice: A survey on ChatGPT and beyond (arXiv:2304.13712). arXiv. https://doi.org/10.48550/arXiv.2304.13712
  3. Yang, S., Kautz, J., & Hatamizadeh, A. (2025). Gated delta networks: Improving Mamba2 with delta rule. In Proceedings of the Thirteenth International Conference on Learning Representations (ICLR 2025) (pp. 29687–29707). https://proceedings.iclr.cc/paper_files/paper/2025/hash/4904fad153f6434a7bcf04465d4be2cc-Abstract-Conference.html
  4. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. In 11th International Conference on Learning Representations (ICLR 2023). https://openreview.net/forum?id=WE_vluYUL-X
  5. Ye, J., Xie, Z., Zheng, L., Gao, J., Wu, Z., Jiang, X., Li, Z., & Kong, L. (2025). Dream 7B: Diffusion large language models (arXiv:2508.15487). arXiv. https://doi.org/10.48550/arXiv.2508.15487
  6. Yedetore, A., Linzen, T., Frank, R., & McCoy, R. T. (2023). How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 9370–9393). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.521
  7. Yu, V. L., Fagan, L. M., Wraith, S. M., Clancey, W. J., Scott, A. C., Hannigan, J., Blum, R. L., Buchanan, B. G., & Cohen, S. N. (1979). Antimicrobial selection by a computer: A blinded evaluation by infectious diseases experts. JAMA, 242(12), 1279–1282. https://doi.org/10.1001/jama.1979.03300120033020
  8. Yudkowsky, E. (2008). Artificial intelligence as a positive and negative factor in global risk. In N. Bostrom & M. M. Ćirković (Eds.), Global catastrophic risks (pp. 308–345). Oxford University Press. https://doi.org/10.1093/oso/9780198570509.003.0021
  9. Yudkowsky, E., & Soares, N. (2017). Functional decision theory: A new theory of instrumental rationality (arXiv:1710.05060). arXiv. https://doi.org/10.48550/arXiv.1710.05060
  10. Yudkowsky, E., & Soares, N. (2025). If anyone builds it, everyone dies: Why superhuman AI would kill us all. Little, Brown and Company.

Z

  1. Zhang, B., & Hora, B. (2025, December 18). T5Gemma 2: The next generation of encoder-decoder models. Google. https://blog.google/technology/developers/t5gemma-2/
  2. Zhang, B., Moiseev, F., Ainslie, J., Suganthan, P., Ma, M., Bhupatiraju, S., Lebron, F., Firat, O., Joulin, A., & Dong, Z. (2025). Encoder-decoder Gemma: Improving the quality-efficiency trade-off via adaptation (arXiv:2504.06225). arXiv. https://doi.org/10.48550/arXiv.2504.06225
  3. Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., Zhang, S., Ghosh, G., Lewis, M., Zettlemoyer, L., & Levy, O. (2023). LIMA: Less is more for alignment. In Advances in Neural Information Processing Systems (Vol. 36, pp. 55006–55021). Curran Associates. https://doi.org/10.52202/075280-2400
  4. Zhu, F., You, Z., Xing, Y., Huang, Z., Liu, L., Zhuang, Y., Lu, G., Wang, K., Wang, X., Wei, L., Guo, H., Hu, J., Ye, W., Chen, T., Li, C., Tang, C., Feng, H., Hu, J., Zhou, J., . . . Wen, J.-R. (2025). LLaDA-MoE: A sparse MoE diffusion language model (arXiv:2509.24389). arXiv. https://doi.org/10.48550/arXiv.2509.24389
  5. Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J. Z., & Fredrikson, M. (2023). Universal and transferable adversarial attacks on aligned language models (arXiv:2307.15043). arXiv. https://doi.org/10.48550/arXiv.2307.15043