Large reasoning models (LRMs) achieve strong performance on complex tasks by generating intermediate reasoning before the final answer, yet they remain prone to reasoning hallucinations such as subtle arithmetic or constraintviolation errors.Prior hallucination detectors often rely on external verification or local tokenlevel signals, which are limited for LRMs and largely overlook whether the cross-phase information flow from reasoning to answering is structurally robust.We propose Routing Focus Score (RFS), a step-level indicator that measures how strongly cross-step attention routing aligns with semantic proximity derived from hidden-state cosine similarity.We further design RFS-Guard, a lightweight hallucination detection framework based on RFS.Empirically, we observe that higher reasoning-answer RFS is consistently associated with higher hallucination risk, suggesting a routing-collapse failure mode where models might prefer selfconfirmation loops and suppress the ability to audit their own generations.Experimental results across multiple domains and models demonstrate the superiority of RFS-Guard for detecting and localizing hallucinations in LRMs without requiring external tools or repeated sampling.

Long-term conversational agents rely on personal memory to maintain coherence and personalization, yet practical systems must operate under context budgets and cope with evolving or contradictory user information. We frame persona memory as a retrieval problem over a growing memory store, and propose REMAP, a reflection-guided memory editing approach for online alignment of persona facts that selectively writes and revises memory entries based on the current dialogue evidence and retrieved related items. The method aims to preserve salient facts while reducing redundancy and resolving apparent conflicts, enabling more efficient context utilization over extended interaction horizons. Experiments on multi-session dialogue datasets show consistent gains in persona-consistent retrieval and response continuity over commonly used memory strategies, while achieving more selective memory updates under comparable operational overhead.