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Wenbin Zhang

NeurIPS 2026×4AAAI 2026×3ACL 2026×2INFOCOM 2026社会与人本安全可解释性强化学习与后训练推理
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0 一作 · 10 篇 · NeurIPS 2026 / AAAI 2026 / ACL 2026 / INFOCOM 2026

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2026 顶会论文

10
作者位次 #3/3NeurIPS 2026Poster· Social Aspects / Fairness, Equity, Justice and Safety

Towards Fair Graph Generation Without Sensitive Attribute

Zichong Wang · Zhipeng Yin · Wenbin Zhang

社会与人本安全OpenReview
作者位次 #28/30NeurIPS 2026Poster· Social Aspects / Accountability, Transparency and Interpretability

Decentralized AI Governance Must Decouple Policy Processing from Capability Enforcement

Hasan Kassem · Christoforos Anagnostopoulos · Alejandro Aristizabal · Spyridon Bakas · Orion G Banks · Omar Benjelloun · Tian Cai · Sergen Cansiz · Davide Chicco · Patrick Foley · Showkot Hossain · Taeho Jung · Peter Kairouz · Yanchen liu · Marco Lorenzi · Peter Mattson · Ann K Novakowski · Michael O'Connor · Holger Roth · Adrish Sannyasi · Charalampos Savvaidis · Micah Sheller · David Solooki · Dimitris Stripelis · Renato Umeton · Marc Vesin · Ray Wang · Wenbin Zhang · Mic Bowman · Alexandros Karargyris

可解释性社会与人本OpenReview
作者位次 #7/10ACL 2026Poster· Natural Language Processing

FLAIR : Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic- A ss I sted Reasoner

Hao Wu · Hongru Sun · Wanqing Li · Xinguo Yu · Hao Ming · Xiao Luo · Wenbin Zhang · Jiahong Zhao · Yi Guo · Jie Yang

摘要

Mathematical reasoning is one of the core capabilities for Large Language Models (LLMs). Yet, existing approaches often rely on static heuristics or pre-determined reasoning strategies, limiting their ability to adapt to different intermediate states. To address this limitation, we propose FLAIR...

推理可解释性强化学习与后训练OpenReview 项目页
作者位次 #3/5ACL 2026Poster· Natural Language Processing

Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLM s in the Financial Domain

Gang Cheng · Haibo Jin · Wenbin Zhang · Haohan Wang · Jun Zhuang

摘要

Large Language Models (LLMs) are increasingly deployed in finance, where unsafe behavior can lead to serious regulatory risks. However, most red-teaming research focuses on overtly harmful content and overlooks attacks that appear legitimate on the surface yet induce regulatory-violating response...

安全评测与基准检索与知识OpenReview 项目页
作者位次 #3/3AAAI 2026Poster· Artificial Intelligence

GUIC: Certified Graph Unlearning with Individual Fairness Guarantees

Zichong Wang · Tongliang Liu · Wenbin Zhang

安全社会与人本OpenReview

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