3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Perception-R1: Advancing Multimodal Reasoning Capabilities of MLLMs via Visual Perception Reward
Tong Xiao, Xin Xu, Zhenya Huang +4
Enhancing the multimodal reasoning capabilities of Multimodal Large Language Models (MLLMs) is a challenging task that has attracted increasing attention in the community. Recently…
cs.CL2025★ 1 cited
Improving In-Context Learning with Reasoning Distillation
Nafis Sadeq, Xin Xu, Zhouhang Xie +4
Language models rely on semantic priors to perform in-context learning, which leads to poor performance on tasks involving inductive reasoning. Instruction-tuning methods based on…
cs.CL2024★ 3 cited
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation
Shuting Wang, Xin Yu, Mang Wang +3
Retrieval-augmented generation (RAG) effectively addresses issues of static knowledge and hallucination in large language models. Existing studies mostly focus on question scenario…