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Yanan Cao

ACL 2026×7AAAI 2026×3ICML 2026KDD 2026安全可解释性推理对齐与偏好检索与知识
新星分资深
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方向广度19
合作网络12
0 一作 · 12 篇 · ACL 2026 / AAAI 2026 / ICML 2026 / KDD 2026

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2026 顶会论文

12
作者位次 #3/3ICML 2026Poster· Deep Learning / Large Language Models

Towards Reliable Marking and Verification of AI-Generated Text via Geometry-aware Sentence-level Watermarking

Yubing Ren · Ping Guo · Yanan Cao

作者位次 #8/8ACL 2026Poster· Natural Language Processing

CIA : Inferring the Communication Topology from LLM -based Multi-Agent Systems

Yongxuan Wu · Xixun Lin · He Zhang · Nan Sun · Kun Wang · Chuan Zhou · Shirui Pan · Yanan Cao

摘要

LLM-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. Central to MAS is the communication topology which governs how agents exchange information internally. Consequently, the security of communication topologies has attracted increasing attention....

安全智能体与工具推理OpenReview 项目页
作者位次 #5/6ACL 2026Poster· Natural Language Processing

Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI -Generated Text Detection

Xiaowei Zhu · Yubing Ren · Fang Fang · Shi Wang · Yanan Cao · Li Guo

摘要

The rapid advancement of large language models has increasingly blurred the boundary between human-written and AI-generated text, raising societal risks such as misinformation dissemination, authorship ambiguity, and threats to intellectual property rights. These concerns highlight the urgent nee...

安全评测与基准可解释性OpenReview 项目页
作者位次 #7/7ACL 2026Poster· Natural Language Processing

EA -Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment

Yixuan Nan · Xixun Lin · Yanmin Shang · Ge Zhang · Zheng Fang · Fang Fang · Yanan Cao

摘要

Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and integration. Traditional EA methods mainly rely on knowledge representation learning, but their performance is often l...

推理可解释性效率与系统OpenReview 项目页
作者位次 #6/6ACL 2026Poster· Natural Language Processing

Do LLM s Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations

Yilong Liu · Xixun Lin · Pengfei Cao · Ge Zhang · Fang Fang · Yanan Cao

摘要

Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to tools that are irrelevant to the user’s query, in which case the desired behavior is to refrain from invocations. In this work, we identify a widespr...

智能体与工具对齐与偏好可解释性OpenReview 项目页
作者位次 #3/6ACL 2026Findings· Findings of ACL

Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework

Zhuoshang Wang · Yubing Ren · Yanan Cao · Fang Fang · Xiaoxue Li · Li Guo

摘要

While watermarking serves as a critical mechanism for LLM provenance, existing secret-key schemes tightly couple detection with injection, requiring access to keys or provider-side scheme-specific detectors for verification. This dependency creates a fundamental barrier for real-world governance,...

安全对齐与偏好可解释性OpenReview 项目页
作者位次 #3/7ACL 2026Findings· Findings of ACL

D ual G uard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack

Hao Li · Yubing Ren · Yanan Cao · Yingjie Li · Fang Fang · Shi Wang · Li Guo

摘要

With the rapid development of cloud-based services, large language models have become increasingly accessible through various web platforms. However, this accessibility has also led to growing risks of model abuse. LLM watermarking has emerged as an effective approach to mitigate such misuse and...

安全可解释性数据与预训练OpenReview 项目页
作者位次 #6/6ACL 2026Findings· Findings of ACL

Cognitive Analysis Graph-Guided Multi-Turn Safety Enhancement for Large Language Models

Lanxue Zhang · Yuqiang Xie · Fang Fang · Yubing Ren · Xuebin Wang · Yanan Cao

摘要

Large Language Models exhibit advanced reasoning capabilities that enable them to address complex tasks, but these capabilities also increase the risk of generating harmful content, particularly in multi-turn dialogues. Existing inference-phase safety alignment methods face three major challenges...

安全推理对齐与偏好OpenReview 项目页

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