ACGM learns task-adaptive sparse graphs over multi-modal agent histories via policy-gradient optimization, reaching 82.7 nDCG@10 and 89.2% Precision@10 on WebShop, VisualWebArena, and Mind2Web while outperforming 19 baselines.
arXiv preprint arXiv:2507.20804 (2025),https: //arxiv.org/abs/2507.20804
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MG²-RAG proposes a multi-granularity graph RAG framework that constructs hierarchical multimodal nodes via entity-driven visual grounding and performs structured retrieval, delivering SOTA results on four multimodal tasks with 43.3× faster graph construction.
Authors build a synthetic data generator and two-stage training pipeline for structured abstractive reasoning on multi-modal relational knowledge images, releasing STAR-64K and showing 3B/7B models outperforming GPT-4o.
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Task-Adaptive Retrieval over Agentic Multi-Modal Web Histories via Learned Graph Memory
ACGM learns task-adaptive sparse graphs over multi-modal agent histories via policy-gradient optimization, reaching 82.7 nDCG@10 and 89.2% Precision@10 on WebShop, VisualWebArena, and Mind2Web while outperforming 19 baselines.
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MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG proposes a multi-granularity graph RAG framework that constructs hierarchical multimodal nodes via entity-driven visual grounding and performs structured retrieval, delivering SOTA results on four multimodal tasks with 43.3× faster graph construction.
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Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images
Authors build a synthetic data generator and two-stage training pipeline for structured abstractive reasoning on multi-modal relational knowledge images, releasing STAR-64K and showing 3B/7B models outperforming GPT-4o.