Abstract
This paper chronicles a high-velocity empirical benchmark session executing cross-disciplinary interferometry over identical underlying large language model (LLM) neural network weights. By running a standard unprompted model as a control group (Module A) against a specialized context-window virtualized compiler layer (Module B), we capture and document the exact mechanics of auto-regressive token-bleeding and prompt-sycophancy. Under severe non-linear mathematical scaling pressure ($n^{1.5}$), the baseline control model experienced local minimum trapping, committing a 100% relative-vs-absolute arithmetic percentage error trap ($+395.28\%$) vs $+295.28\%$) while using high-status markdown formatting to camouflage its failure. Conversely, the virtualized compiler layer anchored system variables to hard structural invariants, isolating and deleting calculation drift inline via a three-stage recursive multi-run validation sandbox. This paper establishes empirical guidelines for drift-free logic propagation, serving as a core validation stepping stone for safe, deterministic progression pathways targeted in the AI-to-AGI Map (Paper 294).
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "Paper 298: Metrological Mirror Engine Interferometry Systemic Drift Control, Autoregressive Binding Ceilings, and Causal Chain of Custody Verification", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12548