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Transforming scientific papers into multi-turn generation trajectories not only doubles the training data but also boosts academic writing benchmarks while maintaining reasoning skills.
Even state-of-the-art MLLMs fail to accurately reconstruct academic documents, revealing a critical gap in machine understanding of scientific knowledge.
Hyper-M2RAG redefines multimodal retrieval by capturing complex relationships in a hypergraph structure, achieving superior performance with less computational overhead.
