用可追溯的信用记录机制，让机器人拒绝不可靠的预测。

Robots are beginning to act on world-model predictions, yet reliability is still expressed through instantaneous, model-internal signals that say whether a prediction looks trustworthy now, not where comparable imagination has already failed. DreamLedger instead treats reliability as a persistent deployment object: an execution-settled credit file recording how often consumed predictions are borne out, indexed by operating condition, region, and prediction horizon, and consulted before each use. Each consumed prediction is registered as a claim and settled against arriving reality without manual labels; the resulting credit gates consumption (low credit shortens reliance or triggers observation), and every reliance event remains auditable via dependency tickets and replayable logs. Persistent credit changes where the gate refuses rather than what the model gets wrong: 69% of denials land on cells that have already failed, episode-local reset triples off-target denials in healthy conditions, and under a localized recurrent degradation persistent credit halves burned imagination, at a cost in task completion. Across three simulated domains, unmodified DreamerV3, TD-MPC2, and V-JEPA 2-AC mounts, and a real Franka, paired quadrotor evaluation shows credit-gated planning reduces burned imagination by 62% (95% CI 43-81%) versus blind consumption; settlement-grounded calibration yields moderate, seed-consistent operating points where raw instantaneous gates collapse to extremes, while persistent books trade verification for reliance (manipulation probes 1.00 to 0.36/episode at success 0.98 vs. 0.94). The trust layer spans decoder-, latent-, and token-space interfaces. On hardware, settlement runs under real sensing and contact noise, both models are priced creditworthy at the frozen 9-cm tolerance, a failure loop is re-priced online, and all 1,062 registered spends replay from the audit logs.