A Universal No-Go Theorem for Unbounded Cognition

This work presents a systematic computational investigation into the physical and logical admissibility of cognition, intelligence, and subjective experience as emergent phenomena in abstract dynamical systems. Without invoking biological assumptions, neural architectures, learning rules, or task-specific optimization, we explore the space of implementation-independent information-processing systems subject only to fundamental constraints such as finite energy dissipation, irreducible noise, causal coherence, signal interference, and self-model consistency. Across all explored configurations, we identify a sharp and universal boundary separating dynamically admissible cognitive regimes from no-go regions in which cognition necessarily collapses. Beyond a critical threshold of functional integration, global accessibility, or self-model transparency, systems undergo unavoidable phase transitions characterized by causal overload, interference instability, loss of temporal coherence, or self-model divergence. These results establish a universal no-go theorem: cognition cannot exist in systems with unbounded integration, perfect global access, or complete self-transparency, independent of substrate or implementation. Intelligence is therefore not maximized by increasing connectivity or precision, but instead requires constrained integration, structured noise, modularity, and partial opacity. The findings impose fundamental limits on both artificial and biological cognitive systems and challenge assumptions underlying unlimited AI scaling, maximal integration theories, and disembodied intelligence.