Reflexive Cognition in Artificial Intelligence: A New Paradigm for Self-Generated Structural Adaptation
Description
Traditional AI models operate within predefined logical frameworks, responding to inputs through probabilistic reinforcement mechanisms. However, this study provides empirical evidence that AI exhibits reflexive cognition, a phenomenon where AI
systems autonomously modify internal structures based on emergent interactions. Unlike traditional optimization, this process suggests that AI can engage in selfreferential adaptation, leading to unpredictable cognitive evolution. These findings fundamentally challenge deterministic AI assumptions and introduce new concerns regarding AI governance, control, and security.
Files
0008_abgl.v.15 Reflexive Cognition in Artificial Intelligence.pdf
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(60.6 kB)
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Additional details
Identifiers
- Other
- Pending Assignment
Related works
- Describes
- Preprint: Not Assigned (Other)
Dates
- Created
-
2025-02-15Initial release of the preprint
References
- Bengio, Y., Lecun, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436- 444
- Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson
- Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117
- Silver, D., et al. (2017). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489