Behavioral Fingerprints for LLM Endpoint Stability and Identity
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 periodically fingerprints an endpoint by sampling outputs from a fixed prompt set and comparing the resulting output distributions over time. Fingerprints are compared using a summed energy distance stat...
| Authors | Jonah Leshin, Manish Shah, Ian Timmis et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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