Mean Articulation Machines

References

• Adner R, Levinthal DA (2008) Doing versus seeing: Acts of exploitation and perceptions of exploration. Strategic Entrepreneurship J. 2(1):43–52.Crossref, Google Scholar
• Ansoff HI (1965) Corporate Strategy: An Analytic Approach to Business Policy for Growth and Expansion (McGraw-Hill, Columbus, OH).Google Scholar
• Apple Machine Learning Research Team (2025) The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity (Apple Inc., Cupertino, CA).Google Scholar
• Asai M, Kajino H, Fukunaga A, Muise C (2022) Classical planning in deep latent space. J. Artificial Intelligence Res. 74:1599–1686.Crossref, Google Scholar
• Bai Y, Chen F, Wang H, Xiong C, Mei S (2023) Transformers as statisticians: Provable in-context learning with in-context algorithm selection. Adv. Neural Inform. Processing Systems 36:2498.Google Scholar
• Bai Y, Tu S, Zhang J, Peng H, Wang X, Lv X, Li J (2025) Longbench v2: Towards deeper understanding and reasoning on realistic long-context multitasks. Proc. 63rd Annual Meeting Assoc. Comput. Linguistics, Long Papers, vol. 1 (Association for Computational Linguistics, Cedarville, OH), 3639–3664.Google Scholar
• Bai Y, Jones A, Ndousse K, Askell A, Chen A, DasSarma N, Drain D, et al. (2022) Training a helpful and harmless assistant with reinforcement learning from human feedback. Preprint, submitted April 12, https://arxiv.org/abs/2204.05862.Google Scholar
• Bamberger PA (2018) AMD—Clarifying what we are about and where we are going. Acad. Management Discoveries 4(1):1–10.Crossref, Google Scholar
• Barney JB (1991) Firm resources and sustained competitive advantage. J. Management 17(1):99–120.Crossref, Google Scholar
• Bender EM, Gebru T, McMillan-Major A, Shmitchell S (2021) On the dangers of stochastic parrots: Can language models be too big? Proc. 2021 ACM Conf. Fairness, Accountability, Transparency (FAccT ’21) (ACM, New York), 610–623.Google Scholar
• Bengio Y (2024) Machine learning and information theory concepts towards a theory of mathematical intelligence. Preprint, submitted March 7, https://arxiv.org/abs/2403.04571.Google Scholar
• Berg JM, Duguid MM, Goncalo JA, Harrison SH, Miron-Spektor E (2023) Escaping irony: Making research on creativity in organizations more creative. Organ. Behav. Human Decision Processes 175:104235.Crossref, Google Scholar
• Bhagavatula C, Le Bras R, Malaviya C, Sakaguchi K, Holtzman A, Rashkin H, Downey D, Yih WT, Choi Y (2020) Abductive commonsense reasoning. 8th Internat. Conf. Learn. Representations (ICLR 2020) (OpenReview).Google Scholar
• Bhardwaj A, Sergeeva A, Mahoney J, Nickerson J (2025) Theorizing as problem solving: A pragmatist perspective on the logic of pursuit. Strategy Sci. 10(4):338–359.Link, Google Scholar
• Bi J (2025) Don’t believe AI hype, this is where it’s actually headed | Oxford’s Michael Wooldridge | AI history. YouTube (March 11), https://www.youtube.com/watch?v=Zf-T3XdD9Z8.Google Scholar
• Binz M, Schulz E (2023) Using cognitive psychology to understand GPT-3. Proc. Natl. Acad. Sci. USA 120(6):e2218523120.Crossref, Google Scholar
• Blohm I, Antretter T, Sirén C, Grichnik D, Wincent J (2022) It’s a people’s game, isn’t it?! A comparison between the investment returns of business angels and machine learning algorithms. Entrepreneurship Theory Practice 46(4):1054–1091.Crossref, Google Scholar
• Boden MA (2004) The Creative Mind: Myths and Mechanisms, 2nd ed. (Routledge, London).Crossref, Google Scholar
• Bommarito MJ II, Katz DM (2022) GPT takes the bar exam. Preprint, submitted December 29, https://doi.org/10.48550/arXiv.2212.14402.Google Scholar
• Bridgelall R (2024) Unraveling the mysteries of AI chatbots. Artificial Intelligence Rev. 57:89.Crossref, Google Scholar
• Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, Neelakantan A, et al. (2020) Language models are few-shot learners. Adv. Neural Inform. Processing Systems 33:159.Google Scholar
• Bubeck S, Chandrasekaran V, Eldan R, Gehrke J, Horvitz E, Kamar E, Lee P, et al. (2023) Sparks of artificial general intelligence: Early experiments with GPT-4. Preprint, submitted April 13, https://arxiv.org/abs/2303.12712.Google Scholar
• Burgelman RA (1983) A process model of internal corporate venturing in the diversified major firm. Admin. Sci. Quart. 28(2):223–244.Crossref, Google Scholar
• Cai K (2023) Databricks CEO Ali Ghodsi’s AI obsession made him a billionaire. Forbes (March 29), https://www.forbes.com/sites/kenrickcai/2023/03/29/databricks-ceo-ali-ghodsi-ai-obsession-billionaire/.Google Scholar
• Cai K, Singh J (2025) Google clinches milestone gold at global math competition, while OpenAI also claims win. Reuters (July 22), https://www.reuters.com/world/asia-pacific/google-clinches-milestone-gold-global-math-competition-while-openai-also-claims-2025-07-22/.Google Scholar
• Carlini N, Tramèr F, Wallace E, Jagielski M, Herbert-Voss A, Lee K, Roberts A, Brown TB, Song D, Erlingsson Ú, Oprea A, Raffel C (2021) Extracting training data from large language models. Bailey M, Greenstadt R, eds. Proc. 30th USENIX Security Sympos. (USENIX Security ’21) (USENIX Association, Berkely), 2633–2650.Google Scholar
• Chalmers D, MacKenzie NG, Carter S (2021) Artificial intelligence and entrepreneurship: Implications for venture creation in the fourth industrial revolution. Entrepreneurship Theory Practice 45(5):1028–1053.Crossref, Google Scholar
• Csaszar FA, Ketkar H, Kim H (2024) Artificial intelligence and strategic decision-making: Evidence from entrepreneurs and investors. Strategy Sci. 9(4):322–345.Link, Google Scholar
• Cyert RM, March JG (1963) A Behavioral Theory of the Firm (Prentice Hall, Hoboken, NJ).Google Scholar
• Dawid A, LeCun Y (2023) Introduction to latent variable energy-based models: A path towards autonomous machine intelligence. Preprint, submitted June 5, https://arxiv.org/abs/2306.02572.Google Scholar
• Dierickx I, Cool K (1989) Asset stock accumulation and sustainability of competitive advantage. Management Sci. 35(12):1504–1511.Link, Google Scholar
• Dunne D, Martin R (2006) Design thinking and how it will change management education: An interview and discussion. Acad. Management Learn. Ed. 5(4):512–523.Crossref, Google Scholar
• Einstein A (1952) Relativity: The Special and the General Theory, 15th ed. (Crown Publishers, New York).Google Scholar
• Eisenhardt KM, Martin JA (2000) Dynamic capabilities: What are they? Strategic Management J. 21(10–11):1105–1121.Crossref, Google Scholar
• Fadell T (2012) The birth of the iPod and the iPhone. McAfee A, Brynjolfsson E, eds. Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy (Harvard Business Review Press, Brighton, MA), 117–125.Google Scholar
• Farrell H, Gopnik A, Shalizi C, Evans J (2025) Large AI models are cultural and social technologies. Science 387(6739):1153–1156.Crossref, Google Scholar
• Felin T, Holweg M (2024) Theory is all you need: AI, human cognition, and causal reasoning. Strategy Sci. 9(4):346–371.Link, Google Scholar
• Felin T, Zenger TR (2017) The theory-based view: Economic actors as theorists. Strategy Sci. 2(4):258–271.Link, Google Scholar
• Felin T, Gambardella A, Stern S, Zenger TR (2020) Lean startup and the business model: Experimentation revisited. Long Range Planning 53(4):101889.Crossref, Google Scholar
• Forrester JW (1961) Industrial Dynamics (MIT Press, Cambridge, MA).Google Scholar
• Foss NJ, Klein PG (2012) Organizing Entrepreneurial Judgment: A New Approach to the Firm (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Franceschelli G, Musolesi M (2025) On the creativity of large language models. AI Soc. 40:3785–3795.Crossref, Google Scholar
• Freeman RE (1984) Strategic Management: A Stakeholder Approach (Pitman, Lanham, MD).Google Scholar
• Frieder S, Pinchetti L, Chevalier A, Griffiths R-R, Salvatori T, Lukasiewicz T, Petersen PC, Berner J (2023) Mathematical capabilities of ChatGPT. Preprint, submitted July 20, https://arxiv.org/abs/2301.13867.Google Scholar
• Gary MS, Wood RE (2011) Mental models, decision rules, and performance heterogeneity. Strategic Management J. 32(6):569–594.Crossref, Google Scholar
• Gary MS, Wood RE, Pillinger T (2012) Enhancing mental models, analogical transfer, and performance in strategic decision making. Strategic Management J. 33(11):1229–1246.Crossref, Google Scholar
• Gavetti G (2005) Cognition and hierarchy: Rethinking the microfoundations of capabilities’ development. Organ. Sci. 16(6):599–617.Link, Google Scholar
• Gavetti G (2012) Toward a behavioral theory of strategy. Organ. Sci. 23(1):267–285.Link, Google Scholar
• Gavetti G, Rivkin JW (2005) How strategists really think: Tapping the power of analogy. Harvard Bus. Rev. 83(4):54–63.Google Scholar
• Gentner D (1983) Structure-mapping: A theoretical framework for analogy. Cognitive Sci. 7(2):155–170.Crossref, Google Scholar
• Gervás P (2024) Computational creativity: A critical perspective. AI Magazine 45(1):12–25.Google Scholar
• Ghemawat P (2002) Competition and business strategy in historical perspective. Bus. History Rev. 76(1):37–74.Crossref, Google Scholar
• Goertzel B (2007) Human-level artificial general intelligence and the possibility of a technological singularity. Artificial Intelligence 171(18):1161–1173.Crossref, Google Scholar
• Greve HR (2003) Organizational Learning from Performance Feedback: A Behavioral Perspective on Innovation and Change (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Hamel G, Prahalad CK (1994) Competing for the Future (Harvard Business School Press, Boston).Google Scholar
• Hanna RE, Smith LR, Mhaskar R, Hanna K (2024) Performance of language models on the family medicine in-training exam. Family Medicine 56(9):555–560.Crossref, Google Scholar
• Hanson NR (1958) Patterns of Discovery: An Inquiry into the Conceptual Foundations of Science (Cambridge University Press, Cambridge, UK).Google Scholar
• Hao S, Gu Y, Ma H, Hong JJ, Wang Z, Wang DZ, Hu Z (2023). Reasoning with language model is planning with world model. Proc. 2023 Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 8154–8173.Google Scholar
• Hofstadter D (2023) Gödel, Escher, Bach, and AI. Atlantic (July 5), https://www.theatlantic.com/ideas/archive/2023/07/godel-escher-bach-geb-ai/674589/.Google Scholar
• Holton G (1973) Thematic Origins of Scientific Thought: Kepler to Einstein (Harvard University Press, Cambridge, MA).Google Scholar
• Holtzman A, Buys J, Du L, Forbes M, Choi Y (2020) The curious case of neural text degeneration. Internat. Conf. Learn. Representations (ICLR 2020) (OpenReview).Google Scholar
• Hsieh C-P, Sun S, Kriman S, Acharya S, Rekesh D, Jia F, Zhang Y, Ginsburg B (2024) RULER: What’s the real context size of your long-context language models? Preprint, submitted August 6, https://arxiv.org/abs/2404.06654.Google Scholar
• Huang H, LeCun Y, Balestriero R (2025) LLM-JEPA: Large language models meet joint embedding predictive architectures. Preprint, submitted October 7, https://arxiv.org/abs/2509.14252.Google Scholar
• Isaacson W (2011) Steve Jobs (Simon & Schuster, New York).Google Scholar
• Ji Z, Lee N, Frieske R, Yu T, Su D, Xu Y, Ishii E, Bang YJ, Madotto A, Fung P (2023) Survey of hallucination in natural language generation. ACM Comput. Surveys 55(12):248.Crossref, Google Scholar
• Jia N, Luo X, Fang Z, Liao C (2024) When and how artificial intelligence augments employee creativity. Acad. Management J. 67(1):5–32.Crossref, Google Scholar
• Kambhampati S (2024) Can large language models reason and plan? Ann. New York Acad. Sci. 1534(1):15–18.Crossref, Google Scholar
• Kambhampati S, Valmeekam K, Guan L, Verma M, Stechly K, Bhambri S, Saldyt L, Murthy A (2024) LLMs can’t plan, but can help planning in LLM-modulo frameworks. Preprint, submitted June 12, https://arxiv.org/abs/2402.01817.Google Scholar
• Kaplan S (2008) Framing contests: Strategy making under uncertainty. Organ. Sci. 19(5):729–752.Link, Google Scholar
• Kaplan RS, Norton DP (1996) The Balanced Scorecard: Translating Strategy into Action (Harvard Business School Press, Boston).Google Scholar
• Kaplan J, McCandlish S, Henighan T, Brown TB, Chess B, Child R, Gray S, Radford A, Wu J, Amodei D (2020) Scaling laws for neural language models. Preprint, submitted January 23, https://arxiv.org/abs/2001.08361.Google Scholar
• Ketokivi M, Mantere S (2010) Two strategies for inductive reasoning in organizational research. Acad. Management Rev. 35(2):315–333.Crossref, Google Scholar
• Kim WC, Mauborgne R (2005) Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant (Harvard Business School Press, Boston).Google Scholar
• Kirzner IM (1973) Competition and Entrepreneurship (University of Chicago Press, Chicago).Google Scholar
• Knight FH (1921) Risk, Uncertainty, and Profit (Houghton Mifflin, Boston).Crossref, Google Scholar
• Kojima T, Gu SS, Reid M, Matsuo Y, Iwasawa Y (2022) Large language models are zero-shot reasoners. Adv. Neural Inform. Processing Systems 35:22199–22213. Google Scholar
• Kurzweil R (2022) The Singularity Is Nearer (Viking, New York).Google Scholar
• Laban P, Hayashi H, Zhou Y, Neville J (2025) LLMs get lost in multi-turn conversation. Preprint, submitted May 9, https://arxiv.org/abs/2505.06120.Google Scholar
• Lamb A, He D, Goyal A, Ke G, Liao C-F, Ravanelli M, Bengio Y (2021) Transformers with competitive ensembles of independent mechanisms. Preprint, submitted February 27, https://arxiv.org/abs/2103.00336.Google Scholar
• Lampinen AK, Dasgupta I, Chan SCY, Sheahan HR, Creswell A, Kumaran D, McClelland JL, Hill F (2024) Language models, like humans, show content effects on reasoning tasks. PNAS Nexus 3(7):233.Crossref, Google Scholar
• LeCun Y (2022a) AI and the limits of language. LinkedIn (September 9), https://www.linkedin.com/posts/yann-lecun_ai-and-the-limits-of-language-activity-6967929903409205248--ypi.Google Scholar
• LeCun Y (2022b) A path towards autonomous machine intelligence. Position paper, OpenReview.Google Scholar
• Levinthal DA (1997) Adaptation on rugged landscapes. Management Sci. 43(7):934–950.Link, Google Scholar
• Lin S, Hilton J, Evans O (2022) TruthfulQA: Measuring how models mimic human falsehoods. Proc. 60th Annual Meeting Assoc. Comput. Linguistics, Long Papers, vol. 1 (Association for Computational Linguistics, Cedarville, OH), 3214–3252.Google Scholar
• Lovallo D, Clarke C, Camerer C (2011) Robust analogizing and the outside view: Two empirical tests of case-based decision making. Strategic Management J. 33(5):496–512.Crossref, Google Scholar
• Makridakis S, Hogarth RM, Gaba A (2010) Why forecasts fail—And what to do instead. MIT Sloan Management Rev. 51(2):83–90.Google Scholar
• March JG (1991) Exploration and exploitation in organizational learning. Organ. Sci. 2(1):71–87.Link, Google Scholar
• March JG, Simon HA (1958) Organizations (Wiley, New York).Google Scholar
• Marcus G (2022) Deep learning is hitting a wall. Nautilus (March 10), https://nautil.us/deep-learning-is-hitting-a-wall-238440/.Google Scholar
• Marcus G, Davis E (2020) Rebooting AI: Building Artificial Intelligence We Can Trust (Vintage, New York).Google Scholar
• Martin RL (2009) The Design of Business: Why Design Thinking Is the Next Competitive Advantage (Harvard Business Press, Boston).Google Scholar
• McBride R, Packard MD, Clark BB (2024) Rogue entrepreneurship. Entrepreneurship Theory Practice 48(1):392–417.Crossref, Google Scholar
• McCoy R, Yao S, Friedman D, Hardy M, Griffiths T (2023) Embers of autoregression: Understanding large language models through the problem they are trained to solve. Preprint, submitted September 24, https://arxiv.org/abs/2309.13638.Google Scholar
• McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophysica 5(4):115–133.Crossref, Google Scholar
• Minsky M (1974) A framework for representing knowledge. MIT Artificial Intelligence Laboratory Memo No. 306, MIT, Cambridge, MA.Google Scholar
• Mintzberg H (1994) The Rise and Fall of Strategic Planning (Free Press, New York).Google Scholar
• Mintzberg H, Waters JA (1985) Of strategies, deliberate and emergent. Strategic Management J. 6(3):257–272.Crossref, Google Scholar
• Mitchell M (2021) Artificial Intelligence: A Guide for Thinking Humans (Picador, New York).Google Scholar
• Mossberg W (2007) The iPhone matches rivals’ features but its main strength may be ease of use. Wall Street J. (June 27), https://www.wsj.com/articles/SB118289311361649057?mod=hp_lead_pos2.Google Scholar
• Nelson RR, Winter SG (1982) An Evolutionary Theory of Economic Change (Harvard University Press, Cambridge, MA).Google Scholar
• Nonaka I, Takeuchi H (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation (Oxford University Press, Oxford, UK).Crossref, Google Scholar
• OpenAI (2023) GPT-4 technical report. Preprint, submitted December 19, https://arxiv.org/abs/2303.08774v4.Google Scholar
• Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, et al. (2022) Training language models to follow instructions with human feedback. Adv. Neural Inform. Processing Systems 35:27730–27744.Google Scholar
• Pais A (1982) Subtle Is the Lord: The Science and the Life of Albert Einstein (Oxford University Press, Oxford, UK).Google Scholar
• Pearl J (2000) Causality: Models, Reasoning, and Inference (Cambridge University Press, Cambridge, UK).Google Scholar
• Peirce CS (1955) Philosophical Writings of Peirce (Dover Publications, Garden City, New York).Google Scholar
• Peteraf MA (1993) The cornerstones of competitive advantage: A resource-based view. Strategic Management J. 14(3):179–191.Crossref, Google Scholar
• Polanyi M (1966) The Tacit Dimension (University of Chicago Press, Chicago).Google Scholar
• Poli M, Massaroli S, Nguyen EN, Fu DY, Dao T, Baccus S, Bengio Y, Ermon S, Ré C (2023) Hyena hierarchy: Towards larger convolutional language models. Preprint, submitted April 19, https://arxiv.org/abs/2302.10866.Google Scholar
• Porter ME (1980) Competitive Strategy: Techniques for Analyzing Industries and Competitors (Free Press, New York).Google Scholar
• Porter ME (1996) What is strategy? Harvard Bus. Rev. 74(6):61–78.Google Scholar
• Porter ME (2008) The five competitive forces that shape strategy. Harvard Bus. Rev. 86(1):25–40.Google Scholar
• Posen HE, Keil T, Kim S, Meissner F (2018) Renewing research on problemistic search: A review and research agenda. Acad. Management Ann. 12(1):208–251.Crossref, Google Scholar
• Powell TC, Lovallo D, Fox CR (2011) Behavioral strategy. Strategic Management J. 32(13):1369–1386.Crossref, Google Scholar
• Rahmandad H, Sterman JD (2012) Reporting guidelines for simulation-based research in the social sciences. System Dynam. Rev. 28(4):396–411.Crossref, Google Scholar
• Raisch S, Krakowski S (2021) Artificial intelligence and management: The automation–augmentation paradox. Acad. Management Rev. 46(1):192–210.Crossref, Google Scholar
• Ren R, Liu Y (2024) Towards understanding how transformers learn in-context through a representation learning lens. Adv. Neural Inform. Processing Systems (NeurIPS 2024), vol. 37 (Neural Information Processing Systems Foundation, Inc., San Diego, CA).Google Scholar
• Repenning NP (2002) A simulation-based approach to understanding the dynamics of innovation implementation. Organ. Sci. 13(2):109–127.Link, Google Scholar
• Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536.Crossref, Google Scholar
• Rumelt RP (1991) How much does industry matter? Strategic Management J. 12(3):167–185.Crossref, Google Scholar
• Schendel DE, Hofer CW (1979) Strategic Management: A New View of Business Policy and Planning (Little, Brown, and Company, Boston).Google Scholar
• Schoemaker PJH (1995) Scenario planning: A tool for strategic thinking. Sloan Management Rev. 36(2):25–40.Google Scholar
• Schumpeter JA (1934) The Theory of Economic Development (Harvard University Press, Cambridge, MA).Google Scholar
• Seals S, Shalin V (2024) Evaluating the deductive competence of large language models. Proc. 2024 Conf. North American Chapter Assoc. Comput. Linguistics: Human Language Technol. (NAACL 2024), Long Papers, vol. 1 (Association for Computational Linguistics, Cedarville, OH), 8614–8630.Google Scholar
• Searle JR (1980) Minds, brains, and programs. Behav. Brain Sci. 3(3):417–424.Crossref, Google Scholar
• Searle JR (1983) Intentionality: An Essay in the Philosophy of Mind (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Searle JR (1990) Is the brain’s mind a computer program? Sci. Amer. 262(1):26–31.Crossref, Google Scholar
• Senge PM (1990) The Fifth Discipline: The Art and Practice of the Learning Organization (Doubleday/Currency, New York).Google Scholar
• Sergeeva A, Bhardwaj A, Dimov D (2021) In the heat of the game: Analogical abduction in a pragmatist account of entrepreneurial reasoning. J. Bus. Venturing 36(6):106158.Crossref, Google Scholar
• Shepherd DA, Majchrzak A (2022) Machines augmenting entrepreneurs: Opportunities (and threats) at the nexus of artificial intelligence and entrepreneurship. J. Bus. Venturing 37(5):106227.Crossref, Google Scholar
• Shoemaker PJH (1996) Scenario planning: A tool for strategic thinking. Sloan Management Rev. 36(2):25–40.Google Scholar
• Simon HA (1957) Models of Man: Social and Rational (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Solow RM (1987) We’d better watch out. [Review of Manufacturing Matters: The Myth of the Post-Industrial Economy, by S. S. Cohen & J. Zysman]. The New York Times Book Rev. 36 (July 12).Google Scholar
• Sterman JD (1989) Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Sci. 35(3):321–339.Link, Google Scholar
• Sterman JD (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World (Irwin/McGraw-Hill, Columbus, OH).Google Scholar
• Surowiecki J (2008) The Wisdom of Crowds (Anchor Books, New York).Google Scholar
• Sutton R (2019) The bitter lesson. Incomplete Ideas (March 13), incompleteideas.net/IncIdeas/BitterLesson.html.Google Scholar
• Tang Z, Kejriwal M (2024) Human-like cognitive patterns as emergent phenomena in LLMs. Preprint, submitted December 20, https://arxiv.org/abs/2412.15501.Google Scholar
• Teece DJ, Pisano G, Shuen A (1997) Dynamic capabilities and strategic management. Strategic Management J. 18(7):509–533.Crossref, Google Scholar
• Thompson C (2012) Smarter Than You Think: How Technology Is Changing Our Minds for the Better (Penguin Press, New York).Google Scholar
• Togelius J, Yannakakis GN (2024) Choose your weapon: Survival strategies for depressed AI academics. Proc. IEEE 112(1):4–11.Crossref, Google Scholar
• Uehara S (2025) Is scaling the key to an AI future? The “scaling hypothesis,” alternative pathways, and the “narrative” we choose to believe. Marubeni Washington Report No. 20, Marubeni Institute.Google Scholar
• Valmeekam K, Stechly K, Kambhampati S (2024) LLMs still can’t plan; can LRMs? A preliminary evaluation of OpenAI’s o1 on PlanBench. Preprint, submitted September 20, https://arxiv.org/abs/2409.13373.Google Scholar
• Valmeekam K, Marquez M, Sreedharan S, Kambhampati S (2023) On the planning abilities of large language models: A critical investigation. Oh A, Neumann T, Globerson A, Saenko K, Hardt M, Levine S, eds. Advances in Neural Information Processing Systems, vol. 36 (Curran Associates, Inc., Red Hook, NY), 75993–76005. Google Scholar
• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Adv. Neural Inform. Processing Systems 30:5998–6008.Google Scholar
• Vogelstein F (2013) Dogfight: How Apple and Google Went to War and Started a Revolution (Farrar, Straus and Giroux, New York).Google Scholar
• Von Oswald J, Niklasson E, Randazzo E, Sacramento J, Mordvintsev A, Zhmoginov A, Vladymyrov M (2023) Transformers learn in-context by gradient descent. Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J, eds., Proc. 40th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 202 (PMLR, New York), 35151–35174.Google Scholar
• Wang X, Wei J, Schuurmans D, Le QV, Chi E, Narang S, Chowdhery A, Zhou D (2023) Self-consistency improves chain-of-thought reasoning in language models. Internat. Conf. Learn. Representations (ICLR 2023) (OpenReview).Google Scholar
• Webb T, Holyoak KJ, Lu H (2023) Emergent analogical reasoning in large language models. Nature Human Behav. 7:1526–1541.Crossref, Google Scholar
• Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, Chi E, Le Q, Zhou D (2022) Chain-of-thought prompting elicits reasoning in large language models. Preprint, submitted October 10, https://arxiv.org/abs/2201.11903v5.Google Scholar
• Weick KE (1989) Theory construction as disciplined imagination. Acad. Management Rev. 14(4):516–531.Crossref, Google Scholar
• Welleck S, Kulikov I, Roller S, Dinan E, Cho K, Weston J (2020) Neural text generation with unlikelihood training. Internat. Conf. Learn. Representations (ICLR 2020) (Open Review).Google Scholar
• Wenger E, Kenett YN (2025) We’re different, we’re the same: Creative homogeneity across LLMs. Preprint, submitted January 31, https://arxiv.org/abs/2501.19361.Google Scholar
• Wernerfelt B (1984) A resource-based view of the firm. Strategic Management J. 5(2):171–180.Crossref, Google Scholar
• Whittaker ET (1951) A History of the Theories of Aether and Electricity, vol. 2 (Thomas Nelson, Nashville, TN).Google Scholar
• Wooldridge M (2018) A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going (Flatiron Books, New York).Google Scholar
• Wooldridge M (2020) A Brief History of Artificial Intelligence (Flatiron Books, New York).Google Scholar
• Wu D, Yang J, Wang K (2024) Exploring the reversal curse and other deductive challenges in large language models. Patterns 5(10):100945.Google Scholar
• Xie SM, Raghunathan A, Liang P, Ma T (2022) In-context learning as implicit Bayesian inference. Internat. Conf. Learn. Representations (ICLR 2022) (OpenReview).Google Scholar
• Yang Z, Du X, Li J, Zheng J, Poria S, Cambria E (2024) Large language models for automated open-domain scientific hypotheses discovery. Findings of the Association for Computational Linguistics: ACL 2024 (Association for Computational Linguistics, Stroudsburg, PA), 13545–13565.Google Scholar
• Yang Z, Liu W, Gao B, Xie T, Li Y, Ouyang W, Poria S, Cambria E, Zhou D (2024) MOOSE-Chem: Large language models for rediscovering unseen chemistry scientific hypotheses. Preprint, submitted October 28, https://arxiv.org/abs/2410.07076v3.Google Scholar
• Zečević M, Willig M, Dhami DS, Kersting K (2023) Causal parrots: Large language models may talk causality but are not causal. Preprint, submitted August 23, https://arxiv.org/abs/2308.13067.Google Scholar
• Zhang W, Zhang R, Guo J, de Rijke M, Fan Y, Cheng X (2024) Pretraining data detection for large language models: A divergence-based calibration method. Proc. 2024 Conf. Empirical Methods Natural Language Processing (EMNLP 2024) (Association for Computational Linguistics, Stroudsburg, PA), 5263–5274.Google Scholar
• Zhou J, Sinha M, Dong J, Li Y, Hu Y (2024a) Enhancing logical reasoning in large language models via extract-then-answer prompting. Preprint, submitted December 16, https://doi.org/10.48550/arXiv.2409.12437.Google Scholar
• Zhou J, Ghaddar A, Zhang G, Ma L, Hu Y, Pal S, Coates M, Wang B, Zhang Y, Hao J (2024b) Enhancing logical reasoning in large language models through graph-based synthetic data. Preprint, submitted December 16, https://arxiv.org/abs/2409.12437.Google Scholar
• Zhou Y, Ye J, Ling Z, Han Y, Huang Y, Zhuang H, Liang Z, et al. (2025) Dissecting logical reasoning in large language models: A fine-grained evaluation and supervision study. Preprint, submitted October 9, https://arxiv.org/abs/2506.04810.Google Scholar