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TAP-RAG, a task-aware policy-controlled RAG framework for long-document multimodal QA, achieves the best overall accuracy among the compared systems, improving over a matched multimodal-RAG baseline.
CaRe is proposed, a training-free robust framework that calibrates compact visual representations before reasoning to preserve semantic fidelity in VLMs and outperforms state-of-the-art token reduction baselines.
An algorithm framework named DR-EFT (Domain-Representative Experts for Fine-Tuning), which explores and loads the domain-representative experts for subsequent retraining and reincorporation and demonstrates robustness through validations on popular MoE LLMs, including Qwen, DeepSeek, and Ernie.
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