GraphMemix: Query-Aware Evidence Forests for Long-Term Multimodal Agent Memory

• URL: http://arxiv.org/abs/2608.26983v1
• Date: Thu, 27 Aug 2026 11:28:49 GMT
• Title: GraphMemix: Query-Aware Evidence Forests for Long-Term Multimodal Agent Memory
• Abstract summary: We propose GraphMemix, a graph memory framework that models memory organization as query-aware evidence-forest construction.<n> Experimental results across four long-term multimodal memory benchmarks demonstrate significant improvements with different foundation models.
• Score: 87.58855368314693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Organizing long-term memory for multimodal agents remains challenging because existing methods either suffer from expensive question-agnostic offline summaries or naive embedding similarity matching that introduces incomplete and redundant context. To address these issues, we propose GraphMemix, a combinatorial-optimization graph memory framework that models memory organization as query-aware evidence-forest construction. Specifically, our method consists of three key components:(1) candidate graph construction, which expands multi-view seed memories through schema and semantic relations to acquire query-aware original context; (2) evidence utility and activation costs, which decouples direct memory support from anchor-conditioned relation verification to suppress redundant or conflicting information; and (3) forest optimization, which jointly selects a forest-format memory context under a maximum evidence budget and its reliable relational structure. By organizing memory into a query-relevant subgraph, the method avoids substantial lifecycle cost and recovers low-similarity complementary evidence. Experimental results across four long-term multimodal memory benchmarks demonstrate significant improvements with different foundation models and establish a new Pareto frontier between accuracy and lifecycle cost.

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