When Experience Leaves a Trace: Consolidation-Dependent Persistence in Artificial Agents
This paper demonstrates that durable, irreversible behavioral divergence in artificial agents emerges only when learning is consolidated into internal parameters rather than relying on external scaffolding, revealing a critical architectural gap where current systems preserve designer-specified viability but fail to discover endogenous states essential for their own persistence.