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Yunbo Cao

ACL 2026×3ICML 2026推理强化学习与后训练评测与基准安全对齐与偏好
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0 一作 · 4 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

4
作者位次 #7/11ACL 2026Poster· Natural Language Processing

D is C al: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs

Bo Zhang · Jiawei Zhang · Cong Gao · Bingxu Han · Minghao Hu · Jun Zhang · Yunbo Cao · Zhunchen Luo · Wen Yao · Guotong Geng · Zhong Wang

摘要

Although large reasoning models (LRMs) exhibit exceptional mathematical reasoning capabilities on clean inputs, their reasoning accuracy drops substantially in the presence of character-level noise such as typographical errors. Critically, their confidence estimates fail to reflect the correspond...

推理评测与基准OpenReview 项目页
作者位次 #4/7ACL 2026Poster· Natural Language Processing

SHARP : Self-adaptive Harmful Category-aware Prompt Generation for Black-box Jailbreaking

Yingjie Xue · Xingyou Xia · Jun Zhang · Yunbo Cao · Dengpan Ye · Guotong Geng · Fei Li

摘要

Large Language Models (LLMs) have been widely applied in various domains such as education and healthcare, making safety assurance crucial. Jailbreak attacks, a method used in red-teaming, can help evaluate and improve the defensive strategies of LLMs. However, existing jailbreak methods often ov...

安全对齐与偏好强化学习与后训练OpenReview 项目页
作者位次 #6/8ACL 2026Findings· Findings of ACL

When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval

Mingxu Tao · Jiawei Hu · Xian Zhou · Wenpeng Hu · Jiajun Cheng · Yunbo Cao · Zhunchen Luo · Guotong Geng

摘要

Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that BM25 continues to serve as a strong baseline...

评测与基准检索与知识社会与人本OpenReview 项目页

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