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cs.CL2025
Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation
Xinping Zhao, Shouzheng Huang, Yan Zhong +4
Retrieval-Augmented Generation (RAG) effectively improves the accuracy of Large Language Models (LLMs). However, retrieval noises significantly undermine the quality of LLMs' gener…
cs.CV2025
VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video Reasoning?
Yuanxin Liu, Kun Ouyang, Haoning Wu +7
Recent studies have shown that long chain-of-thought (CoT) reasoning can significantly enhance the performance of large language models (LLMs) on complex tasks. However, this benef…
cs.CV2025
Fine-Grained Instruction-Guided Graph Reasoning for Vision-and-Language Navigation
Yaohua Liu, Binkai Ou, Rong Fu +2
Vision and Language Navigation (VLN) requires an embodied agent to traverse complex environments by following natural language instructions, demanding accurate alignment between vi…