[
    {
        "id": "osp-22162",
        "type": "article-journal",
        "title": "Reference-Tail Trust:Certified Probability Floors for Learned Updates Inside a Deployed Network",
        "author": [
            {
                "family": "Sadaghiani",
                "given": "Abdolvahab Khalili"
            },
            {
                "family": "Nunez-Yanez",
                "given": "Jose"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/reference-tail-trust-certified-probability-floors-for-learned-updates-inside-a-deployed-network",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Graph neural networks (GNNs) need to exploit improved message passing without surrendering control over predictions already trusted in deployment. We introduce Reference-Tail Trust (RTT), a framework that admits learned updates inside a frozen GNN and certifies the prediction actually served. RTT couples graph-based proposal states with a constrained internal optimizer: each displacement is charged for its worst-case terminal cross-entropy increase through the incumbent's remaining message-passing layers. A trajectory-validated tube and an independent checker enforce per-node probability floors, $p^{\\mathrm{s}}_{ic} \\ge e^{-H_{\\mathrm{row}}} p^{\\mathrm{r}}_{ic}$, and a call-level budget, $\\sum_i w_i D_\\infty(p^{\\mathrm{r}}_i \\| p^{\\mathrm{s}}_i) \\le H^+$, uniformly over labels. Calls whose adapted outputs pass certification require no separate full incumbent rollout; failed certificates trigger whole-call fallback. We derive the exact probability-floor frontier by water-filling, characterize architecture-constrained efficiency, and establish conditions under which internal propagation exploits evidence unavailable to restricted output correctors. In the reported ogbn-arxiv audit, RTT achieves $6.5\\times 10^{-3}$ nats of mean gain per call, with a one-sided 95% regression-rate upper bound of 0.95% and a 95% negative-flip upper bound of 0.51% on the uninspected part of the reserved node population. Its mean gain is 61% of a cross-fitted posterior-based frontier estimate and exceeds the strongest matched one-pass corrector by $+0.9\\times 10^{-3}$ nats. Reported experiments span eight proposals, six graph-incumbent families, structural and temporal graph shifts, and molecular prediction, with additional image and tabular evaluations. RTT makes GNN adaptation a budgeted, certifiable inference decision rather than an unconditional model replacement."
    }
]