2 citations · 2 across the 5 of their papers we have counts for
6 papers
OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe
Kaichen Zhang, Keming Wu, Zuhao Yang +6
Recent advancements in large reasoning models have fueled growing interest in extending such capabilities to multimodal domains. However, despite notable progress in visual reasoni…
Multi-Agent Tool-Integrated Policy Optimization
Zhanfeng Mo, Xingxuan Li, Yuntao Chen +1
Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks. Existing implementations typically rely o…
MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimization
Xingxuan Li, Yao Xiao, Dianwen Ng +15
Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains,…
100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models
Chong Zhang, Yue Deng, Xiang Lin +8
The recent development of reasoning language models (RLMs) represents a novel evolution in large language models. In particular, the recent release of DeepSeek-R1 has generated wid…
MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
Juncheng Wu, Wenlong Deng, Xingxuan Li +12
Medical tasks such as diagnosis and treatment planning require precise and complex reasoning, particularly in life-critical domains. Unlike mathematical reasoning, medical reasonin…
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks
Xingxuan Li, Weiwen Xu, Ruochen Zhao +3
State-of-the-art large language models (LLMs) exhibit impressive problem-solving capabilities but may struggle with complex reasoning and factual correctness. Existing methods harn…