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

TL;DR - GraphMemix is a graph-based long-term memory framework that builds query-aware evidence forests for multimodal agents. It aims to improve retrieval accuracy while reducing the lifecycle costs and context redundancy of offline summaries and embedding-only retrieval.

• Expands seed memories through schema and semantic relationships to recover relevant original context, including low-similarity complementary evidence.
• Separates evidence utility from relation-verification costs to suppress redundant or conflicting information.
• Jointly optimizes evidence selection and relational structure under a fixed evidence budget.
• Across four multimodal memory benchmarks, it improves results with multiple foundation models and establishes a new accuracy–lifecycle-cost Pareto frontier.