Certified Adaptive Refresh: Anytime-Valid Monitoring for Federated Conformal RAG

Abstract:Question-answering services built on retrieval-augmented generation (RAG), in which a language model answers from retrieved documents, are inspected continuously and upgraded repeatedly, so their reliability guarantee must survive both. We study federated conformal RAG: nodes holding private corpora score candidate answers with a shared language model and send compressed scores to a hub that returns an answer set; a miss omits the true answer. We formulate monitoring as a sequential test: alarm when misses exceed a certified bound on the miss rate (the allowance). But a conformal certificate covers one frozen configuration at one look fixed in advance, so it gives no anytime-valid alarm; a threshold calibrated for one model certifies nothing about its replacement; and re-certifying each upgrade at full error level compounds failures. We propose Anytime-FC-RAG with three auditable rules: pick each configuration before its fresh calibration, charge every certificate attempt to one trajectory-wide budget $\delta_{\rm cal}$, and fix threshold, allowance and bet before each query. One betting wealth $E_t$, growing in expectation only when misses exceed the allowance, never resets across deployments. Our certified adaptive-refresh theorem shows that, with evidence budget $\delta_e$, alarming when $E_t$ reaches $1/\delta_e$ has false-alarm probability at most $\delta_e + \delta_{\rm cal}$ under a random number of history-selected, evidence-driven refreshes of model, retriever, corpus, score or threshold; $\delta_{\rm cal}$ pays for wrong certificates. With an exact order-statistic certificate the bound is distribution-free, architecture-agnostic and about misses, not per-input coverage or distribution shift as such. On Qwen2.5/MMLU-Pro the alarm separated audited-harmful from benign shifts completely at a 5% budget; in a model swap, only fresh calibration kept a downgrade's miss rate in check.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2605.29139 [stat.ML]

(or arXiv:2605.29139v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2605.29139 Focus to learn more arXiv-issued DOI via DataCite

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