GraphMemix: Query-Aware Evidence Forests for Long-Term Multimodal Agent Memory
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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.
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GraphMemix: Query-Aware Evidence Forests for Long-Term Multimodal Agent Memory
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Hugging Face upvotes 0
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.