Topological Information Residency in Artificial Neural Networks
Edge-Centric Computation and Empirical Robustness of Dodecaflake Architectures
- Author :
- Guillaume Desvaux · HOPE 'N MIND SASU
- Year :
- 2026
- Reading time :
- 18 min
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.
Keywords
- Mechanistic interpretability
- Memory & information
- Mathematical complexity
- Falsifiability
- Neural networks & LLMs
- Edge-centric architectures
- Robustness & topological resilience
- Complex Systems & Metrology
- Falsifiability & epistemology