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Zhendong Mao

ACL 2026×6ICLR 2026×4AAAI 2026×3CVPR 2026×2KDD 2026SIGIR 2026检索与知识对齐与偏好多模态评测与基准智能体与工具
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0 一作 · 17 篇 · ACL 2026 / ICLR 2026 / AAAI 2026 / CVPR 2026 / KDD 2026 / SIGIR 2026

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2026 顶会论文

17
作者位次 #6/7ACL 2026Poster· Natural Language Processing

FS -Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents

Chiwei Zhu · Benfeng Xu · Mingxuan Du · Shaohan Wang · Xiaorui Wang · Zhendong Mao · Yongdong Zhang

摘要

Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing, and preventing effective test-time scali...

检索与知识推理智能体与工具OpenReview 项目页
作者位次 #4/4ACL 2026Poster· Natural Language Processing

LAF a CT : Attribution-based Localization and Focused Sequential Analysis of Fact-Critical Tokens for Hallucination Detection

Xin Wang · Jiahao Li · Licheng Zhang · Zhendong Mao

摘要

Large Language Models (LLMs) suffer from hallucinations, severely undermining their reliability. While white-box hallucination detection methods that leverage hidden states prevail, they fail to identify and focus on fact-critical information when analyzing token sequences. To address this, we pr...

安全评测与基准检索与知识OpenReview 项目页
作者位次 #3/4ACL 2026Poster· Natural Language Processing

Zero-Shot Detection of LLM -Generated Text using Temperature Sensitivity

Shixuan Ma · Jiahao Li · Zhendong Mao · Quan Wang

摘要

The widespread deployment of Large Language Models (LLMs) has spurred significant progress in the detection of LLM-generated text. However, existing detection methods often rely on statistical features that are insufficient for reliable detection; for example, even though LLM-generated and human-...

代码可解释性数据与预训练OpenReview 项目页
作者位次 #2/3ACL 2026Poster· Natural Language Processing

C ode R ipple: Wavelet-Based Detection of LLM -Generated Code

Xingyu Yao · Zhendong Mao · Quan Wang

摘要

Detecting LLM-generated code is crucial for ensuring software provenance, security, reliability, and licensing compliance. Existing training-free detectors, mostly adapted from text-based methods, rely on global statistics of the Token Perplexity Sequence (TPS) and struggle with code. We reveal a...

代码评测与基准安全OpenReview 项目页
作者位次 #7/7ACL 2026Findings· Findings of ACL

W ild G raph B ench: Benchmarking G raph RAG with Wild-Source Corpora

Pengyu Wang · Benfeng Xu · Licheng Zhang · Shaohan Wang · Mingxuan Du · Chiwei Zhu · Zhendong Mao

摘要

Graph-based Retrieval-Augmented Generation (GraphRAG) organizes external knowledge as a hierarchical graph, enabling efficient retrieval and aggregation of scattered evidence across multiple documents. However, many existing benchmarks for GraphRAG rely on short, curated passages as external know...

检索与知识评测与基准效率与系统OpenReview 项目页
作者位次 #2/5ACL 2026Findings· Findings of ACL

Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation

Jiaang Li · Zhendong Mao · Quan Wang · Yuning Wan · Yongdong Zhang

摘要

Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated context. However, LLMs often exhibit a stylistic bias when presented with mixed contexts, favoring fluent but hallucinated generated content over fact...

检索与知识对齐与偏好安全OpenReview 项目页
作者位次 #6/6SIGIR 2026Poster· Information Retrieval (Short)

GraphSynthQA: Knowledge-Graph-Guided Query Synthesis and Step-Level Preference Optimization for Web Agents

Chiwei Zhu · Mingxuan Du · Benfeng Xu · Shengzhuo zhang · Xiaorui Wang · Zhendong Mao

摘要

Web browsing—widely used for information retrieval and fact verification—has become a fundamental capability of recently emerged large language model (LLM) agents, which is often elicited by training on complex questions requiring web search. However, this task faces challenges with respect to da...

检索与知识对齐与偏好强化学习与后训练OpenReview
作者位次 #4/4AAAI 2026Poster· Artificial Intelligence

LayerEdit: Disentangled Multi-Object Editing via Conflict-Aware Multi-Layer Learning

Fengyi Fu · Mengqi Huang · Lei Zhang · Zhendong Mao

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