Gate-Logged Self-Auditing Control for Adaptive Compute, Routing, Memory, and Trajectory Management

This 29-page paper introduces a master tracking system designed to connect and control multiple separate AI models. Normally, when an AI makes a decision such as looking up an external memory, choosing a specific processing path, or terminating a simulation, it does so silently in the background. To make this process transparent, this architecture introduces four distinct operational "gates" (Compute, Route, Memory, and Control). Before the AI takes an action at any gate, it must write an internal "entry note" predicting its outcome; once the action is complete, it audits its performance at the exit gate to calculate its own "regret" and learn from its mistakes. The core experimental finding exposes a critical "confidence trap" in learned AI controllers. Testing revealed that the AI’s internal confidence and its actual real-world accuracy frequently disagree; the model can be highly confident in its written notes while its actual task performance tanks. The data proves that letting a neural network make unconstrained routing decisions cannot reliably beat simple, stable, rule-based systems. Instead, the paper concludes that autonomous controllers function best not as independent decision-makers, but as structured supervisors held in check by strict budget limits and clear safety rules. All code, research notes, and LLM co-reasoning logs are fully documented on page one.