Self-consuming generative models go MAD
Open accessAbstract
Seismic advances in generative AI algorithms have led to the temptation to use AI-synthesized data to train next-generation models. Repeating this process creates autophagous (“self-consuming”) loops whose properties are poorly understood. We conduct a thorough analysis using state-of-the-art generative image models of three autophagous loop families that differ in how they incorporate fixed or fresh real training data and whether previous generations' samples have been biased to trade off data quality versus diversity. Our primary conclusion across all scenarios is that without enough fresh real data in each generation of an autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease. We term this condition Model Autophagy Disorder (MAD) and show that appreciable MADness arises in just a few generations.
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Data ambiguity might precipitate the recycling of LLM-generated data in the training processes of subsequent models, which could adversely impact text generation quality and destabilize the research landscape.
“An additional potential risk was articulated by Alemohammad et al. (2023), who posited that data ambiguity might precipitate the recycling of LLM-generated data in the training processes of subsequent models. This scenario could adversely impact the text generation quality of these emergent LLMs, thereby destabilizing the research landscape dedicated to the detection of LLM-generated text.”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Alemohammad et al. (2023)
The Introduction and Discussion (pages not available in our copy) say that models can unknowingly train on AI-generated data and that repeated synthetic-data training can reduce quality or diversity; the Discussion explicitly extends the concern to text. The paper does not attribute this recycling specifically to “data ambiguity” or say it would destabilize research on detecting LLM-generated text. Its experiments use image models, not LLM text generation. (LLM, conf=0.86)
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Elsayed, M., Ryu, J., Vero, J., & Torres, E. B. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15100463 · Cited by 2 · see citing works
Synergizing linguistic features and transformer networks for detecting AI-generated text
Yadagiri, A., Kumar, P., Poddar, Y., Pakray, P., & Chunka, C. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02637-6 · Cited by 2 · see citing works
Outlier and collapse: The enron corpus and foundation model training data
Zimmer, Z. (2026) · Big Data & Society · doi:10.1177/20539517261421474 · Cited by 2 · see citing works
What is a digital author? Generative AI, concealment, and notes for the emergent moment
Shah, N. (2026) · Communication and the Public · doi:10.1177/20570473261416537 · Cited by 2 · see citing works
Spennemann, D. H. R. (2026) · Heritage · doi:10.3390/heritage9040124 · Cited by 2 · see citing works
Lange, M., Koliousis, A., Fayez, F., Gogarty, E., & Twumasi, R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0315768 · Cited by 3 · see citing works
Algorithms, Bytes, and Chips: The Emerging Political Economy of Foundation ModelsPreprint
Mills, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4834417 · Cited by 2 · see citing works
ARTIFICIAL INTELLIGENCE IN HUMAN LIFE: PERSON OR INSTRUMENT
Gazniuk, L., Бейлін, М. В., & Soina, I. (2024) · The Journal of V N Karazin Kharkiv National University Series Philosophy Philosophical Peripeteias · doi:10.26565/2226-0994-2024-71-7 · Cited by 1 · see citing works
The superalignment of superhuman intelligence with large language models
Huang, M., Wang, Y., Cui, S., Ke, P., & Tang, J. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4348-6 · Cited by 1 · see citing works
Synthetic data as meaningful data. On Responsibility in data ecosystems
Capasso, M. (2025) · Big Data & Society · doi:10.1177/20539517251386053 · Cited by 1 · see citing works
Straňák, P. (2026) · Symmetry · doi:10.3390/sym18050764 · Cited by 1 · see citing works
SPGNet: A Serial-Parallel Gated Convolutional Network for Image Classification on Small Datasets
Song, Y., Wang, J., & Wang, M. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651059 · Cited by 1 · see citing works
Die (produktive) Rolle von generativer KI im erziehungswissenschaftlichen Diskurs
Herzig, B. (2025) · Erziehungswissenschaft · doi:10.3224/ezw.v36i1.02 · Cited by 1 · see citing works
Future-Present Learning in Place: postdigital learning at the scale of the cityPreprint
Lister, P. (2024) · doi:10.31235/osf.io/ruds4 · Cited by 1 · see citing works
A Sentence-Semantic-Based Watermark for Large Language Models Against Paraphrasing AttacksPreprint
Wang, H., Zhao, L., Lyu, X., Gao, L., Song, J., & Shen, H. T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4774832
Więcej niż „trudny problem świadomości”: o możliwości powstania świadomej sztucznej inteligencji
Przybysz, P. (2025) · Człowiek i Społeczeństwo · doi:10.14746/cis.2024.58.4
CaCOM: customizing text-to-image diffusion models in the wild via continual active selection
Yang, J., Zhang, Y., Wang, H., Chen, G., Wu, S., & Zhao, J. (2025) · Machine Learning · doi:10.1007/s10994-025-06743-y
Tanaka, T. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04714-1
Entwicklungsrichtungen bei Drohnen und deren Umwelteffekte
Schulze, H. (2025) · Springer eBooks · doi:10.1007/978-3-662-70921-4_5
Einfluss von Drohnenflügen auf die Tierwelt
Schulze, H. (2025) · Springer eBooks · doi:10.1007/978-3-662-70921-4_8
Adapt & Align: Continual Learning with Generative Models’ Latent Space Alignment
Deja, K. R., Cywiński, B., Rybarczyk, J., & Trzcinski, T. P. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130748
Klingner, S., Zill, M., & Fischer, G. (2025) · International Communication Gazette · doi:10.1177/17480485251327469
Einsatz KI-gestützter Systeme für Literaturreviews Explorative Analyse und kritische Reflexion
Michael Fellmann, M. A., Bongertmann, H., Götz, N., & Pommerencke, C. (2025) · Ars digitalis · doi:10.1007/978-3-658-45839-3_7 · Cited by 1 · see citing works
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.002.0005
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.002.0007
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0007
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0001
Taxonomies of Cognitive Architectures
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0004
Practical Applications of Cognitive Architectures
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0010
Cognitive Architectures, AI, and Cognitive Science
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0003
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.002.0004
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0008
Challenges of the Past and Opportunities Ahead
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0013
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0006
Cognitive Architectures in the Deep Learning Era
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0012
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0009
What Are Cognitive Architectures?
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0002
Evaluating Cognitive Architectures
Kotseruba, I., & Tsotsos, J. K. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192844835.003.0011
Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?
Baily, M. N., Byrne, D. M., Kane, A. T., & Soto, P. E. (2025) · Finance and Economics Discussion Series · doi:10.17016/feds.2025.053 · Cited by 4 · see citing works
The Ouroboros effect and heterodox domains
Vukov, J. M., Lebkuecher, G., Joseph, T. L., Martinez, E. M., Ramirez, M., & Burns, M. B. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09850-5
Torres, E. B., & Elsayed, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.09.674997
Desire and Data: Metabolism of AI
Epstein, V. (2025) · Baltic screen media review./Baltic screen media review · doi:10.2478/bsmr-2025-0002
When Competition Consumes Diversity: A Theoretical Framework for Generative Collapse
Wang, T. (2025) · International Conference on Mathematics and Machine Learning (ICMML) · doi:10.1145/3783779.3783839
Lockhart, E. N. S. (2026) · Symmetry · doi:10.3390/sym18030518
Kasneci, G., & Kasneci, E. (2026) · AI and Ethics · doi:10.1007/s43681-026-01132-0 · Cited by 1 · see citing works
A New Setting for Human Endeavors
Sagasti, F. (2026) · Innovation, technology and knowledge management · doi:10.1007/978-3-032-20668-8_9
Despotović, M., Brunauer, W., & Koch, D. (2026) · Journal of Property Investment and Finance · doi:10.1108/jpif-07-2026-0148
Luo, Y., Zhang, Z., & Huang, Y. (2026) · Buildings · doi:10.3390/buildings16173549
LABORATORIUM KONWERSACJI – JAK RÓŻNE MODELE JĘZYKOWE INTERPRETUJĄ RZECZYWISTOŚĆ
Grabiec, A., & Łyszkowska, E. (2026) · International Journal of Legal Studies ( IJOLS ) · doi:10.5604/01.3001.0055.9361
- Authors
- Casco-Rodriguez, J., Alemohammad, S., Luzi, L., Imtiaz, A., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R.
- Affiliations
- Rice University (US)
- ORCID
- Venue
- LatinX in AI at Neural Information Processing Systems Conference 2023
- Publisher
- Journal of LatinX in AI Research
- Published
- 2023-12-10
- OpenAlex
- W4390430574
- Cited by
- 83
- References
- 54
- Citations by year
- 2026: 13 · 2025: 46 · 2024: 18 · 2023: 6
- FWCI
- 8.88
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- LatinX in AI at Neural Information Processing Systems Conference (LXAI) (publishedVersion)
- Rice Research Repository (Rice University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- self-training, generative image models, synthetic data, generative AI training, model degradation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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