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Minhao Cheng

ACL 2026×2ICLR 2026多模态对齐与偏好智能体与工具强化学习与后训练数据与预训练
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合作网络12
0 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
作者位次 #20/21ICLR 2026Poster

WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning

Kuan Li · Zhongwang Zhang · Huifeng Yin · Rui Ye · Yida Zhao · Liwen Zhang · Litu Ou · Ding-Chu Zhang · Xixi Wu · Xinmiao Yu · Jialong Wu · Xinyu Wang · Zile Qiao · Zhen Zhang · Yong Jiang · Pengjun Xie · Fei Huang · Zhiqin Xu · Shuai Wang · Minhao Cheng · Jingren Zhou

智能体与工具强化学习与后训练数据与预训练OpenReview
作者位次 #7/9ACL 2026Poster· Natural Language Processing

A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning

Tianyu Yang · Sihong Wu · Yilun Zhao · Zhenwen Liang · Lisen Dai · Chen Zhao · Minhao Cheng · Arman Cohan · Xiangliang Zhang

摘要

Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems involving both textual and visual modalities. However, current models still face significant challenges in real-world visual math tasks, often misinterpreting diag...

推理多模态对齐与偏好OpenReview 项目页
作者位次 #5/5ACL 2026Findings· Findings of ACL

Empowering Reliable Visual-Centric Instruction Following in MLLM s

Weilei He · Feng Ju · Zhiyuan Fan · Rui Min · Minhao Cheng

摘要

Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user-specified intentions. Nevertheless, existing benchmarks for evaluating MLLMs’ instruction-following capability prima...

多模态评测与基准对齐与偏好OpenReview 项目页

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