Verified State Visibility as a Learning Signal for Block-Pacing Control of Permissioned Ledgers in AI-Native 6G Edge Networks: A Simulation Study

Artificial intelligence (AI)-native sixth-generation (6G) networks are expected to rely on agents in different administrative domains that coordinate policies, model versions and access rights through shared state. A permissioned ledger can anchor this state, but an edge agent can act on an update only when its own read node returns it. We use this verified state visibility, measured at the controlled read nodes, as both the service objective and the learning signal for block-pacing control in a permissioned Avalanche layer-one (L1) chain. The predictive components—gradient-boosted quantile regression with a split-conformal bound—are established; the contribution is the signal against which they are trained and checked. In simulated 6 h episodes with non-stationary load and transport degradations, a risk-aware controller that respects the rate limit of Avalanche Community Proposal 226 (ACP-226) reduced validator central processing unit (CPU) load by about 18% relative to static 250 ms pacing at a service-level objective (SLO) violation rate of about 2%. Trained on transaction receipts instead, the same controller violated the objective in about two of three windows. A reactive rule driven by verified reads performed comparably, so the gain stems from the learning signal rather than from learning-based control. In a case study, verified reads exposed an injected calibration fault online. All results are simulated.