Persona-driven role-playing agents tend to drift from their assigned profile or contradict their earlier statements as the dialogue lengthens, a faithfulness failure we call persona inconsistency hallucination. Existing inference-time mitigations apply reward-guided decoding uniformly at every token, while we observe that inconsistency risk is concentrated on a small subset of turns and is locally predictable, which makes a selective intervention feasible. We propose TaDI (Targeted Decoding Intervention), a training-free method with three components: an Inconsistency Risk Predictor that gates intervention before generation, a Targeted Reward-Guided Decoding module that combines a positive profile reward with a negative commitment reward, and a Commitment Propagation Memory that tracks the agent’s prior claims across turns. We additionally introduce a Type-A/B/C violation taxonomy distinguishing profile violation, history contradiction, and commitment fabrication, used as a diagnostic for attributing where any method gains or loses. Extensive experiments show TaDI is competitive with the strongest always-on baseline while being substantially more efficient and more robust under adversarial interrogation.

As Generative Engines revolutionize information retrieval by synthesizing direct answers from retrieved sources, ensuring source visibility becomes a significant challenge. Improving it through targeted content revisions is a practical strategy termed Generative Engine Optimization (GEO). However, optimizing a document for diverse queries presents a constrained optimization challenge where heterogeneous queries often impose conflicting and competing revision requirements under a limited content budget. To address this challenge, we propose IF-GEO, a “diverge-then-converge” framework comprising two phases: (i) mining distinct optimization preferences from representative latent queries; (ii) synthesizing a Global Revision Blueprint for guided editing by coordinating preferences via conflict-aware instruction fusion. To explicitly quantify IF-GEO’s objective of cross-query stability, we introduce risk-aware stability metrics. Experiments on multi-query benchmarks demonstrate that IF-GEO achieves substantial performance gains while maintaining robustness across diverse retrieval scenarios.