Memory Echo in Single-Agent Long-Term Memory: Lifecycle Evaluation and Provenance-Guided Mitigation
Motivated by research in social cognition and memory psychology on repeated exposure, retrieval, and revision, we define Memory Echo as the excess retrieval exposure gained by historical information from the same source without additional independent evidence, and introduce MEchoBench to evaluate its emergence, correction resistance, persistence, and effect on answers.
ProofPaper ↗
Key points
- Long-term memory supports agents across sessions.
- Repeated retrieval and write-back can let one historical claim occupy multiple retrieval positions and influence current answers without independent evidence.
- Existing work examines retrieval, updating, and question answering, leaving same-source record accumulation and post-correction persistence underexplored.
- In paired replay on 190 fixed questions across four memory frameworks, FPA raises accuracy from 0.825 to 0.920 with DeepSeek V4 Flash and from 0.782 to 0.916 with Kimi K2.6; mean Decision Contamination Effect falls from 0.106 to 0.030 and from 0.109 to 0.038, respectively.
Sources (1)
- [1]Memory Echo in Single-Agent Long-Term Memory: Lifecycle Evaluation and Provenance-Guided MitigationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:44 AM
Motivated by research in social cognition and memory psychology on repeated exposure, retrieval, and revision, we define Memory Echo as the excess retrieval exposure gained by historical information from the same source without additional independent evidence, and introduce MEchoBench to evaluate its emergence, correction resistance, persistence, and effect on answers.
Long-term memory supports agents across sessions.
Extractive summary: sentences quoted from the sources.
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