According to Me: Long-Term Personalized Referential Memory QA
This addresses the challenge of realistic personalized memory for AI assistants, though it is incremental as it builds on existing memory systems with a new benchmark and method.
The paper tackles the problem of personalized AI assistants recalling and reasoning over long-term, multimodal user memories by introducing ATM-Bench, a benchmark with four years of privacy-preserving data, and finds poor performance (under 20% accuracy) on hard queries, with their Schema-Guided Memory method improving over prior approaches.
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