MemFuse: Multi-Source Memory Fusion from Fragmented Observations

2026-08-19

AI Summary

研究针对多源记忆融合问题，提出MemFuse系统和MemFuseBench基准，通过结构化记忆保存来源证据，并在跨源证据融合任务上表现优异。

Abstract

Long-term memory is essential for agents that operate across extended interactions, yet existing memory systems and benchmarks predominantly focus on single-source textual histories. In realistic settings, however, relevant information is often fragmented across applications and devices, as well as across users and time, requiring agents to integrate dispersed observations into coherent episodic memories while preserving their source provenance. To address these gaps, we introduce **MemFuseBench**, a benchmark for *multi-source memory fusion*. MemFuseBench is built with a Scene-to-Sensor pipeline that synthesizes controllable scenarios into source-tagged observations, evidence-grounded questions, and adversarial distractors. It enables systematic evaluation of temporal reasoning, cross-source evidence fusion, and robustness to noise. We further propose **MemFuse**, a structured memory system that preserves source-level evidence in event-layer atomic memory and organizes related atomic events into cluster-layer fused memory within a causal fusion graph. During retrieval, MemFuse retrieves and organizes related evidence fragments while maintaining traceability to original source events. Experiments on MemFuseBench show that MemFuse achieves the best overall performance among the evaluated memory systems under all three LLM settings and consistently improves performance on questions requiring cross-source evidence fusion.

Problem

• multi-source memory fusion
• fragmented observations
• source provenance

Innovation

• multi-source memory fusion
• MemFuse
• MemFuseBench
• causal fusion graph
• cross-source evidence