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.

Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses through discussion. However, MAD suffers from a critical vulnerability known as shared misconception: when a majority of agents initially converge on an incorrect answer, the debate process tends to amplify rather than correct the error. Existing methods primarily address peer skew but leave the agents’ inherently biased concept priors unaddressed. To mitigate this systematic weakness, we propose **R²-MAD** (Remember and Reweight for Multi-Agent Debate), a framework that equips agents with an experience memory accumulated from past debates. R²-MAD intervenes on both failure modes through two complementary mechanisms: A debate-state-aware retrieval policy dynamically calibrates the concept prior by retrieving relevant historical evidence based on the current consensus level. Then these retrieved experiences provide a basis for estimating per-agent reliability, yielding confidence weights to modulate peer influence. Experiments on various benchmarks show that R²-MAD achieves consistent improvements over existing single-agent and MAD baselines.