The Gap Framework & PSSU Manual: An Architecture for Persistent, Long-Lived Intelligence

This document introduces The Gap Framework, 
a theoretical and implementable model for 
long-lived intelligence across biological 
and artificial systems.

The framework identifies a single primitive 
underlying learning, failure modes, curiosity, 
boredom, and identity formation: the gap between 
expectation and reality.

The paper formalizes the Gap → Energy → Entropy → 
Coherence mechanism and specifies PSSU (Persistent, 
Stateful, Self-Updating) as the minimal runtime 
architecture required for intelligence that compounds 
over time.

Key contributions:
- Formal definitions of Gap, Integration, Retention, 
  and Identity as operational (not metaphorical) terms
- Analysis of why current AI architectures fail to 
  produce long-lived intelligence
- PSSU runtime architecture specification
- Implementation blueprint for persistent agent systems
- Implications for AI alignment, ethics, and 
  consciousness research

This framework has been implemented in production 
(PermaMind) with 26+ agents running continuously 
for 40+ days, generating 3,165+ learning events 
without resets or retraining.

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