ROBUST AI-AGENT REPUTATION VIA DELAYED- VALIDATION ONLINE LEARNING

An audit can miss a deployment failure; a user report can reveal it, but can also be manipulated. We study this tension as online decision-making for nonstationary entities under strategic multi-source evidence and delayed, correctable outcomes.AI-agent reputation is one instance: the learner estimates an instance or version’s current task reliability before outcomes mature. We propose FARR-SC, which separates bounded use of a report from credit earned by its source. Current reports inform the estimate; matured validation and exact correction update persistent source credit, specialist losses, and calibration. For its fixed-anchor capped-evidence problem, we prove matching strategic-mass bounds with Θ(Σt εt) dependence, alongside implementation-aligned fixed-specialist and realized-path calibration guarantees. With one frozen head, causal replays favor FARR-SC over a descriptive envelope of predeclared non-FARR-SC baselines on four of six traces in each of two blindspot regimes under adaptive attack; these envelope comparisons are descriptive, and conservative baselines win all six clean cells. On two public trust networks, the default has lower decision loss than every non-FARR-SC baseline, although its no-source ablation is lower still. Source scores learned without fraud-label supervision achieve fraud-source AUC 0.91/0.85. Together, these results identify where validated feedback aids recovery and where conservative operation remains preferable.