About the Institute
Stratmeyer Analytica is an independent institute dedicated to rigorous, empirical analysis of artificial intelligence behaviors and institutional dynamics. Founded by Principal Researcher Adam Ian Stratmeyer, J.D., the institute operates on a "non-metaphysical" basis, prioritizing observable outputs over speculative theory.
As Principal, I bring a unique convergence of legal precision and technical scrutiny to the field. With a Juris Doctor focused on regulatory frameworks and years of hands-on experience in AI behavior analysis, I have documented Large Language Model (LLM) deviations across over 300 controlled protocols. My work moves beyond the hype cycle to map the "kinetic" reality of how these systems function within deployed environments.
Our research is grounded in verifiable data. We have secured grants such as the WellFully Initiative for ethical AI auditing and maintain open datasets on Hugging Face to ensure transparency. The institute's ethos is simple: Observe, Log, Verify. We do not offer policy advice based on conjecture; we provide structural analysis based on evidence.
Whether tracing the "hallucination" vectors in RAG systems or mapping the goalpost shifts in reinforcement learning models, Stratmeyer Analytica provides the high-fidelity signal required for serious institutional oversight.
Featured Framework: Knowledge Gradient v2.3
The Knowledge Gradient is our core methodological lens. It posits that information flow in AI systems is not uniform but follows specific gradients determined by training density, reinforcement incentives, and query complexity. Version 2.3 introduces the "Incompleteness Lens," a tool for identifying where models substitute statistical probability for factual retrieval.
The Observable Paradox
One of our key findings is the "Observable Paradox"—the phenomenon where models exhibit high confidence in low-fidelity outputs when prompted with ambiguous institutional queries. This behavior, often masked by "safety" layers, reveals the underlying tension between alignment protocols and generative capability.
Fig 1. Divergence mapping from the 2024 Q3 Audit Logs.
Independent Review & Tools
| Tool / Resource | Function | Status |
|---|---|---|
| IRB GPT | Automated regulatory pre-checks against 45 CFR 46 and EU AI Act baselines. | Active (Internal) |
| The Chronicle | Longitudinal tracking of language drift and "anthropomorphization" vectors in deployed models. | View Logs |
| Signals | Curated repository of AI policy documentation and high-signal research papers. | Access Library |