A black-box monitoring system that uses behavioral 'fingerprints' to detect silent updates or identity shifts in LLM API endpoints.
arXiv · March 20, 2026 · 2603.19022
The Takeaway
Standard uptime/latency metrics fail when providers update weights, quantization, or routing behind a static API. This system allows developers to programmatically verify if the 'GPT-4o' they are calling today is behaviorally identical to the one they used for testing, preventing silent regressions in AI applications.
From the abstract
The consistency of AI-native applications depends on the behavioral consistency of the model endpoints that power them. Traditional reliability metrics such as uptime, latency and throughput do not capture behavioral change, and an endpoint can remain "healthy" while its effective model identity changes due to updates to weights, tokenizers, quantization, inference engines, kernels, caching, routing, or hardware. We introduce Stability Monitor, a black-box stability monitoring system that period