According to Me: Long-Term Personalized Referential Memory QA

• URL: http://arxiv.org/abs/2603.01990v1
• Date: Mon, 02 Mar 2026 15:42:29 GMT
• Title: According to Me: Long-Term Personalized Referential Memory QA
• Authors: Jingbiao Mei, Jinghong Chen, Guangyu Yang, Xinyu Hou, Margaret Li, Bill Byrne,
• Abstract summary: ATM-Bench is the first benchmark for multimodal, multi-source personalized referential Memory QA.<n>Guided Memory (SGM) structurally represents memory items originated from different sources.<n>We find poor performance (under 20% accuracy) on the ATM-Bench-Hard set.
• Score: 27.402232752643275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized AI assistants must recall and reason over long-term user memory, which naturally spans multiple modalities and sources such as images, videos, and emails. However, existing Long-term Memory benchmarks focus primarily on dialogue history, failing to capture realistic personalized references grounded in lived experience. We introduce ATM-Bench, the first benchmark for multimodal, multi-source personalized referential Memory QA. ATM-Bench contains approximately four years of privacy-preserving personal memory data and human-annotated question-answer pairs with ground-truth memory evidence, including queries that require resolving personal references, multi-evidence reasoning from multi-source and handling conflicting evidence. We propose Schema-Guided Memory (SGM) to structurally represent memory items originated from different sources. In experiments, we implement 5 state-of-the-art memory systems along with a standard RAG baseline and evaluate variants with different memory ingestion, retrieval, and answer generation techniques. We find poor performance (under 20\% accuracy) on the ATM-Bench-Hard set, and that SGM improves performance over Descriptive Memory commonly adopted in prior works. Code available at: https://github.com/JingbiaoMei/ATM-Bench

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