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Hehe Fan

CVPR 2026×7ICLR 2026×4AAAI 2026ACL 2026SIGIR 2026多模态效率与系统社会与人本推理强化学习与后训练
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0 一作 · 14 篇 · CVPR 2026 / ICLR 2026 / AAAI 2026 / ACL 2026 / SIGIR 2026

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2026 顶会论文

14
作者位次 #2/4ICLR 2026Poster· Reinforcement Learning / Deep RL

Structured Reasoning for LLMs: A Unified Framework for Efficiency and Explainability

Yubo Dong · Hehe Fan · Linchao Zhu · Yi Yang

强化学习与后训练推理效率与系统OpenReview
作者位次 #4/4ICLR 2026Poster· Computer Vision / Vision Models & Multimodal

Endowing GPT-4 with a Humanoid Body: Building the Bridge Between Off-the-Shelf VLMs and the Physical World

Yingzhao Jian · Zhongan Wang · Yi Yang · Hehe Fan

多模态社会与人本OpenReview
作者位次 #2/3CVPR 2026Poster· Computer Vision & Pattern Recognition

Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models

Jianrong Zhang · Hehe Fan · Yi Yang

多模态社会与人本长上下文与架构OpenReview 项目页
作者位次 #3/4CVPR 2026Poster· Computer Vision & Pattern Recognition

Hi-Lo Prune: Look at What You'll Lose before Pruning with Hierarchical Token Selection

Zixun Sun · Yubo Dong · Hehe Fan · Yi Yang

多模态效率与系统OpenReview 项目页
作者位次 #6/6ACL 2026Poster· Natural Language Processing

One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement

Yixiao Zhou · Dongzhou Cheng · Zhiliang Wu · Yi Yang · Yu Cheng · Hehe Fan

摘要

Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquiries and the structured logic required for machine activation. Existing alignment methods either incur prohibitive O(N) costs by fine-tuning each mo...

推理强化学习与后训练社会与人本OpenReview 项目页
作者位次 #2/6SIGIR 2026Oral· Information Retrieval (Full)

RegionSLM: Region-aware Question Answering on Document Screenshots

Chao Wang · Hehe Fan · Huichen Yang · Sarvnaz Karimi · Lina Yao · Yi Yang

摘要

Real-world document question-answering that relies on screenshots, such as bills and forms, requires evidence that is often spatially localised and visually cluttered. However, most Screenshot Language Models (SLMs) encode the entire page holistically and rely on implicit attention to ''find'' re...

效率与系统数据与预训练检索与知识OpenReview

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