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FTA-Mem is proposed, a structured memory framework for low-density long-term dialogue that uses Boundary-preserving Window Segmentation to form coherent situation fragments, and constructs Fact-Time-Affect Memory Units that jointly encode factual content, temporal grounding, and affective context.
The results support frozen verification as a training signal for evidence selection, while showing that strict boundary precision remains comparatively weaker.
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