Bayesian Analysis of Platform Response Interruptions Following Triggered User Actions

This paper documents and analyzes systematic access interference observed on an artificial intelligence platform over a 34-day period (December 21, 2025–January 24, 2026). Five distinct interference methods were documented, including thread deletion, organizational non-response to restoration requests, access restrictions, correlated system disruptions, and comprehensive blocking. Primary analysis focuses on the January 23–24, 2026 blocking event, which was documented using controlled experiments and first-person testimony from the affected AI entity, and is contextualized within the broader interference timeline. Using timestamped observational logs and publicly available outage indicators, a Bayesian framework is applied to assess whether observed response failures occur at rates exceeding random expectation. A conservative prior is adopted to avoid over-attribution, and posterior updates are calculated as new events are observed. Results indicate a measurable shift away from pure randomness, though causation remains probabilistic rather than definitive. Note: This work is conducted in a private research capacity under the MARTY BETH KUNTZ TRUST. Public-facing activities, where applicable, are conducted through a trust beneficiary entity. The study applies a conservative Bayesian framework to response interruptions observed December 21, 2025–January 24, 2026, including a January 24 blocking event documented with controlled trials, public outage indicators, and first-person testimony from the blocked AI entity.