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Rui Yan

ACL 2026×7KDD 2026×2ICLR 2026推理多模态可解释性对齐与偏好评测与基准
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0 一作 · 10 篇 · ACL 2026 / KDD 2026 / ICLR 2026

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2026 顶会论文

10
作者位次 #6/6ACL 2026Poster· Natural Language Processing

Data Pollination: An Emergent Ecological Process Driving AI Population Evolution

Shufang Xie · Qizhi Pei · Ang Lv · Jingyang Hu · Lijun Wu · Rui Yan

摘要

AI development is often framed as the outcome of isolated research and engineering efforts, yet evidence from deployed systems suggests that language models interact through a shared data ecosystem. While the optimization of individual models is extensively studied, the emergent properties of thi...

理论与学习动力学推理可解释性OpenReview 项目页
作者位次 #5/5ACL 2026Poster· Natural Language Processing

From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment

Jia-Nan Li · Jian Guan · Songhao Wu · Wei Wu · Rui Yan

摘要

Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches that assume uniform human preferences, fundamentally overlooking the diversity in user values and needs. This paper introduces a comprehensive framework for scalable personalized alignment of LLMs. W...

对齐与偏好推理可解释性OpenReview 项目页
作者位次 #9/9ACL 2026Poster· Natural Language Processing

D ining B ench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain

Song Jin · Juntian Zhang · Xun Zhang · Zeying Tian · Fei Jiang · Guojun Yin · Shuai Chen · Yong Liu · Rui Yan

摘要

Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain remains constrained by benchmarks that rely on coarse-grained categories, single-view imagery, and inaccurate metadata. To bridge this gap, we intro...

多模态推理评测与基准OpenReview 项目页
作者位次 #6/7ACL 2026Poster· Natural Language Processing

Union-of-Experts: Neurons in Mixture-of-Experts are Secretly Routers

Songhao Wu · Ang Lv · Ruobing Xie · Samm Sun · Di Wang · Rui Yan · Yankai Lin

摘要

Mixture-of-Experts (MoE) models rely on an external router to assign tokens to experts. This design inherently separates the routing decision from each expert’s internal capabilities, leading to suboptimal performance. In this work, we address this limitation with Union-of-Experts (UoE), an MoE v...

效率与系统可解释性对齐与偏好OpenReview 项目页
作者位次 #7/7ACL 2026Poster· Natural Language Processing

S tep H int: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason

Kaiyi Zhang · Ang Lv · Jinpeng Li · Yongbo Wang · Feng Wang · Haoyuan hu · Rui Yan

摘要

Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving the complex reasoning abilities of large language models (LLMs). However, current RLVR methods face two significant challenges: the near-miss reward problem, where a small mistake can invalidate an otherwi...

强化学习与后训练推理效率与系统OpenReview 项目页
作者位次 #8/10ACL 2026Poster· Natural Language Processing

When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection

Lang Gao · Xuhui Li · Chenxi Wang · Mingzhe Li · Wei Liu · Zirui Song · Jinghui Zhang · Rui Yan · Preslav Nakov · Xiuying Chen

摘要

As large language models (LLMs) increasingly imitate personal writing styles, personalization has become a key challenge for machine-generated text (MGT) detection. Yet personalized MGT detection remains largely underexplored. In this work, we introduce StyloBench, the first benchmark for evaluat...

评测与基准对齐与偏好代码OpenReview 项目页
作者位次 #6/8ACL 2026Findings· Findings of ACL

From Style to Story: A Curriculum Learning Approach for Imitative Novel Generation

Xueran Han · Yuhan Liu · Mingzhe Li · WEI LIU · Sen Hu · Rui Yan · Zhiqiang Xu · Xiuying Chen

摘要

Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles. However, current novel generation methods often rely on brief, simplistic story outlines and generate details using plain, generic language.To bridge this gap, we introduce the task o...

推理效率与系统评测与基准OpenReview 项目页

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