CTNet Reimagines AI as Persistent State Evolution
Ginés Espín Flores introduced CTNet, an architectural framework shifting AI computation from sequential processing to persistent state transitions. The model incorporates reentrant memory and multi-scale coherence, viewing output as a projection of a richer underlying computational background.
CTNet is a bold attempt to ground AI architecture in formal state-transition theory, moving away from the "black box" nature of current models toward a more structured, persistent memory paradigm. By framing computation as an evolution of state, it addresses fundamental questions in reasoning and memory. While mathematically ambitious, the architecture must prove it can match the empirical performance and scalability of established models like Transformers.
DISCOVERED
3h ago
2026-04-23
PUBLISHED
6h ago
2026-04-23
RELEVANCE
AUTHOR
afatcat7999