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Xian-Ling Mao

ACL 2026×3AAAI 2026SIGIR 2026评测与基准检索与知识推理效率与系统数据与预训练
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0 一作 · 5 篇 · ACL 2026 / AAAI 2026 / SIGIR 2026

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2026 顶会论文

5
作者位次 #2/5ACL 2026Poster· Natural Language Processing

Your Reasoning Model Knows What Counts: Self-Guided Chain-of-Thought Pruning for Efficient Reasoning

Zi-Ao Ma · Xian-Ling Mao · Tian Lan · Chen Xu · Zhijing Wu

摘要

Chain-of-Thought (CoT) reasoning is crucial for the performance of Large Reasoning Models (LRMs) but is often hindered by redundant and distracting segments, which incur excessive inference costs and degrade robustness. Existing approaches try to solve this problem by enforcing brevity through ex...

推理效率与系统评测与基准OpenReview 项目页
作者位次 #2/5ACL 2026Poster· Natural Language Processing

EXCEEDS : Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain

Yi-Fan Lu · Xian-Ling Mao · Bo Wang · Xiao Liu · Heyan Huang

摘要

It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, finance, and biology. However, event extraction in scientific domain is still insufficiently supported by comprehensive datasets and tailored methods. Comp...

数据与预训练检索与知识代码OpenReview 项目页
作者位次 #14/15ACL 2026Poster· Natural Language Processing

MTAVG -Bench: A Diagnostic Benchmark for Multi-Talker Dialogue-Centric Audio-Video Generation

Yanghao Zhou · Haitian Li · Rexar Lin · Heyan Huang · Jinxing Zhou · Changsen Yuan · Tian Lan · Ziqin Zhou · Yudong Li · Jiajun Xu · Jingyun Liao · YiMing Cheng · Xuefeng Chen · Xian-Ling Mao · Yousheng Feng

摘要

Recent advances in text-to-audio-video (T2AV) generation have enabled models to synthesize audio-visual videos with multi-participant dialogues. However, existing evaluation benchmarks remain largely designed for human-recorded videos or single-speaker settings. As a result, structural failures i...

多模态评测与基准社会与人本OpenReview 项目页
作者位次 #4/4SIGIR 2026Poster· Information Retrieval (Short)

G-CoS: An Interpretable Gain-Cost Framework for User Satisfaction Estimation in Generative Information Retrieval

Jia-Ling Shi · Zhijing Wu · Yidong Liang · Xian-Ling Mao

摘要

User satisfaction serves as a key indicator of search experience in Generative Information Retrieval (GenIR) systems, and its accurate estimation is essential for system optimization. Extensive research in traditional IR has established interaction signals (e.g., dwell time, clicks, query reformu...

检索与知识可解释性评测与基准OpenReview

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