Can Agents Relive Past Experiences? Diagnosing Event-Affect Decoupling in Affective-Episodic Retrospection

Tingna Xie, Ming Wang, Shi Feng, Yongkang Liu

As memory-augmented agents increasingly support long-term and personalized interactions, reliable recall of emotional experiences while preserving their associated affect becomes necessary. However, existing evaluations usually assess emotion accuracy and factual memory grounding separately, overlooking event-affect correspondence. In this work, we formulate Affective-Episodic Retrospection (AER) as a task that explicitly evaluates the recovery of past events, associated affects, and their correspondence, and define Event-Affect Decoupling as a failure mode where the correct emotion is recalled but associated with the wrong episode. To diagnose Event-Affect Decoupling, we propose EM-TRACE, a stage-wise framework that decomposes AER into retrieval, affect recovery, and realization. On a controlled GoEmotions-based benchmark, Emotion-only retrieval achieves 28.65% affect recovery but retrieves the target episode for only 4.44% of queries, yielding a UAR of 24.21%. Further analysis reveals that event retrieval and affect assignment introduce distinct binding failures. These findings show that reliable AER requires grounded recall of emotional experiences, where episode retrieval,…