@article{desvaux2026dodecaflake,
  title        = {Topological Information Residency in Artificial Neural Networks},
  author       = {Desvaux, Guillaume Jacques Yoann},
  year         = {2026},
  publisher    = {HOPE 'N MIND SASU},
  url          = {https://www.hopenmind.com/publications/en/paper-dodecaflake/},
  doi          = {10.5281/zenodo.20367235},
  keywords     = {Mechanistic interpretability, Memory \& information, Mathematical complexity, Falsifiability, Edge-centric architectures, Robustness \& topological resilience, Falsifiability \& epistemology},
  abstract     = {Experimental evidence that information in trained neural networks resides topologically within edges (weights) rather than nodes (neurons). Introduces Hexaflake and Dodecaflake architectures: 75\% of propagation facets can be ablated with zero performance loss, demonstrating unprecedented topological protection.},
  language     = {english},
}