A Memory Gate for AI: Policy-Bounded Acceptance in the Executable Cognitive Kernel
Summary
Dynamic AI systems face a hidden failure mode: they can learn from their own mistakes. If every output is allowed into memory, stochastic errors do not stay local they accumulate.
In earlier posts, I argued that AI systems should not be trusted to enforce their own correctness.
Modern models are stochastic. They produce correct outputs, partially correct outputs, and completely incorrect outputs, but they do not reliably distinguish between them. That means a system that stores everything it generates will eventually learn from its own mistakes.