SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

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 ac

Read original source ↗ Open in workspace

recordType

paper

region

Global