AROps Research
Published research from the Probabilistic Resilience Engineering (PRE) research program. All papers are published as open science with full experimental protocols, data descriptions, and measurement instrument specifications to enable community replication and extension.
We present the first empirical measurement of Mean Time to Epistemic Failure (MTEF) for autonomous multi-agent AI systems. Across 1,100+ agent generations, 4,182 tracked claims, and four model architectures, we discover the autoimmune paradox: governed systems with active verification decay faster than ungoverned ones. We derive and validate a 72-hour failure horizon through Monte Carlo simulation (10,000 trials, 72.0 ± 23.0h) and develop measurement instruments for real-time epistemic health monitoring.
Information Geometry and Entropy Accounting for extrinsically stabilized multi-agent systems. Extends Paper 1 findings with Fisher Information Matrix analysis, phase transition characterization, and conservation laws for governance-constrained epistemic systems.
Seven trust boundaries, six signal states, and the Agent Trust Protocol (ATP). Formalizes the Open Audit Framework (OAF) as an open standard for transparency-based AI agent governance.
About this research program: The Trust Thermodynamics series applies reliability engineering principles to the epistemic integrity of autonomous AI systems. All experimental protocols, measurement instruments, and data descriptions are published to enable community replication, challenge, and extension. This work is part of Probabilistic Resilience Engineering (PRE).
For practitioner-accessible analysis of these findings, see AROps Insights.