UnfoldMem: Unfolding Multimodal Conversational Memory via Iterative Multi-Agent Interaction

Long-term memory has become an increasingly important component of modern AI systems, enabling agents to leverage information from past interactions. More recent studies have shifted from passive retrieval toward active memory navigation, enabling agents to iteratively access relevant information from long-term memory. However, these approaches conduct both memory retrieval and evidence interpretation within the shared context, which entangles retrieval process with reasoning towards answer, making focused evidence interpretation increasingly difficult. To address this limitation, we propose UnfoldMem, a multi-agent framework that decouples memory exploration from evidence interpretation for multimodal conversational memory. Given a user query, the main agent progressively explores memory by selecting memory access tools and formulates focused sub-questions. Each sub-question is answered by a temporary sub-agent over question-specific context without further exploration, and its response guides main agent's subsequent memory access and final answer generation. Experiments on MemEye and Mem-Gallery demonstrate that UnfoldMem achieves state-of-the-art performance on multimodal long-term memory question answering. Extensive experiments and ablations further validate the effectiveness of each component of our framework and demonstrate its adaptive tool-use behavior across different questions.