CFP last date
30 November 2026
Call for Paper
December Edition
JAAI solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 30 November 2026

Submit your paper
Know more
The week's pick
Responsive image
Architecting Behavioural Anomaly Detection for Cloud-Scale Security Operations: An Interpretable Statistical Framework for SOC-Driven Cyber Threat Analytics

Justice Opara-Martins
Reseach Article

Creativity as Differential Recombination in Generative AI: A Cognitive Semiotic Framework for Evaluating LLM and GAN

by Michela Ravelli
journal cover thumbnail

Journal of Advanced Artificial Intelligence
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 7
Year of Publication: 2026
Authors: Michela Ravelli

Michela Ravelli . Creativity as Differential Recombination in Generative AI: A Cognitive Semiotic Framework for Evaluating LLM and GAN. Journal of Advanced Artificial Intelligence. 2, 7 ( Oct 2026), 36-44. DOI=None

@article{ None,
author = { Michela Ravelli },
title = { Creativity as Differential Recombination in Generative AI: A Cognitive Semiotic Framework for Evaluating LLM and GAN },
journal = { Journal of Advanced Artificial Intelligence },
issue_date = { Oct 2026 },
volume = { 2 },
number = { 7 },
month = { Oct },
year = { 2026 },
pages = { 36-44 },
numpages = {9},
url = { https://jaaionline.phdfocus.com/archives/volume2/number7/creativity-as-differential-recombination-in-generative-ai-a-cognitive-semiotic-framework-for-evaluating-llm-and-gan/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-10-30T22:21:59.394120+05:30
%A Michela Ravelli
%T Creativity as Differential Recombination in Generative AI: A Cognitive Semiotic Framework for Evaluating LLM and GAN
%J Journal of Advanced Artificial Intelligence
%V 2
%N 7
%P 36-44
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The study of machine creativity remains one of the most contested and least resolved questions in contemporary AI research. Existing frameworks tend either to exclude generative systems from the domain of creativity on ontological grounds, or to admit them at the cost of definitional vacuity, resting on what Stephensen calls “a highly anthropocentric ontology” that presupposes, rather than argues for, a sharp segregation between the human and the non-human [1]. This paper proposes a third way grounded in cognitive semiotics: creativity as differential recombination within a constrained semiotic space, developed from the convergence of conceptual blending theory [2], Eco’s encyclopaedia [3], Lotman’s semiosphere [4], and Kristeva’s intertextuality [5]. Large Language Models and Generative Adversarial Networks are shown to instantiate this model in architecturally distinct but formally analogous ways. From this framework, four semiotic evaluative dimensions are derived—differential recombination, semiosphere participation, intertextual transformation, and interpretive openness—articulated in terms of sign-processes rather than mental states, offering a tractable, non-metaphysical basis for assessing creativity in human and machine outputs alike.

References
  1. Jan Løhmann Stephensen. Artificial creativity: Artificial Creativity. Transformations, 36:19–37, 2022.
  2. Gilles Fauconnier and Mark Turner. The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. Basic Books, New York, 2002.
  3. Umberto Eco. Semiotics and the Philosophy of Language. Indiana University Press, Bloomington, 1984.
  4. Jurij Michajlovič Lotman. Universe of the Mind: A Semiotic Theory of Culture. Indiana University Press, Bloomington, 1990.
  5. Julia Kristeva. Sēmeiōtikē; recherches pour une sémanalyse. Paris, Éditions du Seuil, 1969.
  6. Umberto Eco. The Search for the Perfect Language. Blackwell, Oxford, 1995. Translation of La ricerca della lingua perfetta nella cultura europea, 1993; frequently cited as “1997” referring to the paperback reprint.
  7. Stefano Bartezzaghi. Mettere al mondo il mondo: Tutto quanto facciamo per essere detti creativi e chi ce lo fa fare. Bompiani, Milano, 2021.
  8. Agostino d’Ippona. La città di Dio. 426. Edizione italiana di riferimento: Milano, Bompiani, 2001.
  9. Rob Pope. Creativity: Theory, History, Practice. Routledge, London, 2005.
  10. Andreas Reckwitz. The Invention of Creativity: Modern Society and the Culture of the New. Polity Press, Cambridge, UK, 2017. Trans. Steven Black.
  11. Claudio Paolucci, Flavio Valerio Alessi, and Luigi Lobaccaro. The myth of the mad genius: a semiotic perspective on creativity and madness. RIFL - Rivista Italiana di Filosofia del Linguaggio, 2025.
  12. Ada Lovelace. Notes by the translator [to l. f. menabrea, “sketch of the analytical engine invented by charles babbage”]. Scientific Memoirs, Selected from the Transactions of Foreign Academies of Science and Learned Societies, 3:666–731, 1843. Commonly cited as “1842” in secondary literature referring to the composition date.
  13. A. Michael Noll. Human or machine: A subjective comparison of Piet Mondrian’s “Composition with Lines” (1917) and a computer-generated picture. The Psychological Record, 16(1):1–10, 1966.
  14. Erik E. Guzik, Christian Byrge, and Christian Gilde. The originality of machines: AI takes the torrance test. Journal of Creativity, 33(3):100065, 2023.
  15. Jon McCormack, Toby Gifford, and Patrick Hutchings. Autonomy, authenticity, authorship and intention in computer generated art. International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART), 2019.
  16. Margaret A. Boden. The Creative Mind: Myths and Mechanisms. Routledge, London, 2nd edition, 2004.
  17. David Hellberg. Sacred signs or empty words: A naturalistic and semiotic comparison of AI vs human generated art. IntechOpen, 2025.
  18. Luca Miracchi Titus. Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy. Cognitive Systems Research, 83, 2024.
  19. Benjamin Kemple. Semiosis in the machine: Lost natures and artificial intelligence. Filosofi(e)Semiotiche, 10(1):24–39, 2023.
  20. Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, pages 610–623, New York, NY, USA, March 2021. Association for Computing Machinery.
  21. Mark A. Runco and Garrett J. Jaeger. The standard definition of creativity. Creativity Research Journal, 24(1):92–96, 2012.
  22. Mark A. Runco. AI can only produce artificial creativity. Journal of Creativity, 33(3), 2023.
  23. M. Beatrice Fazi. Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics. Rowman & Littlefield International, London, 2018.
  24. Michael North. Novelty: A History of the New. University of Chicago Press, Chicago, IL, 2013. Paperback edition 2015.
  25. Umberto Eco. Combinatoria della creatività. Conferenza tenuta a Firenze per la Nobel Foundation, 15 settembre 2004, 2004. Consultato il 3 ottobre 2025.
  26. Arthur Koestler. The Act of Creation. Hutchinson, London, 1964.
  27. Margaret A. Boden. Computer models of creativity. AI Magazine, 30(3):23–34, 2009. DA VERIFICARE.
  28. Claudio Paolucci. Cognitive Semiotics: Signs, Mind, and Culture. Springer, 2021.
  29. Ferdinand de Saussure. Cours de linguistique générale. Payot, 1916.
  30. Julia Kristeva. Desire in Language: A Semiotic Approach to Literature and Art. Columbia University Press, New York, 1980. Ed. Leon S. Roudiez.
  31. Dario Compagno. Intervento al séminaire international de sémiotique. Paris, Association Française de Sémiotique (29/11/2023), 2023.
  32. Claudio Paolucci. The myth of meaning: Generative AI as language-endowed machines and the machinic essence of the human being. Semiotica, 2025(262):5–23, 2025.
  33. Melanie Mitchell. Artificial intelligence hits the barrier of meaning. Information, 10(2):51, 2019.
  34. John R. Searle. Minds, brains, and programs. Behavioral and Brain Sciences, 3(3):417–424, 1980.
  35. Blaise Agüera y Arcas. Do large language models understand us? Daedalus, 151(2):183–197, 2022.
  36. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative Adversarial Networks, June 2014.
  37. Martin Zeilinger. Generative adversarial copy machines. Culture Machine, 20:1–23, 2021.
  38. Davide Picca. Not Minds, but Signs: Reframing LLMs through Semiotics, March 2025. arXiv preprint v1: May 2025; revised versions through 2026.
  39. Elad Vromen. Language Models as Semiotic Machines: Reconceptualizing AI Language Systems through Structuralist and Post-Structuralist Theories of Language, October 2024.
  40. Jurij M. Lotman. La semiosfera. L’asimmetria e il dialogo nelle strutture pensanti. Marsilio, Venezia, 1985.
  41. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Advances in Neural Information Processing Systems, 30, 2017.
  42. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The Curious Case of Neural Text Degeneration, February 2020.
  43. Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers, November 2021.
  44. Long et al. Ouyang. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155, 2022.
  45. Wang et al. Self-Instruct: Aligning Language Models with Self-Generated Instructions. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki, editors, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13484–13508, Toronto, Canada, July 2023. Association for Computational Linguistics.
  46. Joon Sung et al. Park. Generative Agents: Interactive Simulacra of Human Behavior, August 2023.
  47. Charles et al. Packer. MemGPT: Towards LLMs as Operating Systems, February 2023. arXiv preprint, October 2023; published 2024.
  48. Xinbo et al. Gao. A survey of graph edit distance. Pattern Analysis and Applications, 13(1):113–129, 2010.
  49. Umberto Eco. Opera aperta: Forma e indeterminazione nelle poetiche contemporanee. Bompiani, Milano, 1962.
  50. Charles Sanders Peirce. Collected Papers of Charles Sanders Peirce. Harvard University Press, Cambridge, MA, 1931–1958. Ed. Charles Hartshorne, Paul Weiss, and Arthur W. Burks, 8 vols.
  51. Ashwin K. et al. Vijayakumar. Diverse beam search: Decoding diverse solutions from neural sequence models, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Computational creativity conceptual blending LLMs GANs semiotic machines differential recombination generative AI Eco Fauconnier-Turner novelty

Powered by PhDFocusTM