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UNSW Sydney
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MMEACR achieves significant performance improvements in visually grounded recommendations by effectively integrating multimodal memory and collaborative reasoning.
Stop drowning your MLLMs in irrelevant document noise: FES-RAG shows that carefully selecting multimodal fragments as evidence boosts performance by up to 27% while shrinking context length.
Semantic grounding, not token probability, is the key to better multimodal RAG.
Agentic RAG gets a check-up: Doctor-RAG surgically repairs failures in reasoning trajectories, boosting accuracy while slashing token consumption compared to brute-force reruns.
