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arXiv · 2509.18436

Memory-QA: Answering Recall Questions Based on Multimodal Memories

Hongda Jiang·Xinyuan Zhang·Siddhant Garg·Rishab Arora·Shiun-Zu Kuo·Jiayang Xu·Ankur Bansal·Christopher Brossman·Yue Liu·Aaron Colak·Ahmed Aly·Anuj Kumar·Xin Luna Dong
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Abstract

We introduce Memory-QA, a novel real-world task that involves answering recall questions about visual content from previously stored multimodal memories. This task poses unique challenges, including the creation of task-oriented memories, the effective utilization of temporal and location information within memories, and the ability to draw upon multiple memories to answer a recall question. To address these challenges, we propose a comprehensive pipeline, Pensieve, integrating memory-specific augmentation, time- and location-aware multi-signal retrieval, and multi-memory QA fine-tuning. We created a multimodal benchmark to illustrate various real challenges in this task, and show the superior performance of Pensieve over state-of-the-art solutions (up to 14% on QA accuracy).

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Hongda Jiang, Xinyuan Zhang, Siddhant Garg, Rishab Arora, Shiun-Zu Kuo, Jiayang Xu, Ankur Bansal, Christopher Brossman, Yue Liu, Aaron Colak, Ahmed Aly, Anuj Kumar, Xin Luna Dong. 2025-09-22. Memory-QA: Answering Recall Questions Based on Multimodal Memories. https://arxiv.org/abs/2509.18436

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