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Xu Jinan

ACL 2026×4ICML 2026推理多模态强化学习与后训练多语言与语言学检索与知识
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0 一作 · 5 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

5
作者位次 #7/8ACL 2026Poster· Natural Language Processing

Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning

Xue Zhang · Yunlong Liang · Fandong Meng · Songming Zhang · Kaiyu Huang · Yufeng Chen · Xu Jinan · Jie Zhou

摘要

Large Reasoning Models (LRMs) have achieved remarkable performance on complex reasoning tasks by adopting the “think-then-answer” paradigm, which enhances both accuracy and interpretability. However, current LRMs exhibit two critical limitations when processing non-English languages: (1) They oft...

强化学习与后训练推理多语言与语言学OpenReview 项目页
作者位次 #7/10ACL 2026Findings· Findings of ACL

Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation

Rui Qi · Fengran Mo · Yufeng Chen · Xue Zhang · Shuo Wang · Hongliang Li · Xu Jinan · Meng Jiang · Jian-Yun Nie · Kaiyu Huang

摘要

Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multilingual collections. However, most existing studies employ a unitive process where queries of equivalent semantics across different languages are process...

检索与知识强化学习与后训练对齐与偏好OpenReview 项目页
作者位次 #7/8ACL 2026Findings· Findings of ACL

Can Multi-agent Help Disambiguation in Multi-domain Translation?

Zhibo Man · Shaoyang Xu · Yujie Zhang · Yi Feng · Yuanmeng Chen · Yufeng Chen · Xu Jinan · Wenxuan Zhang

摘要

Large language models (LLMs)-based multi-agent systems have recently shown strong potential for machine translation (MT). However, their application to multi-domain translation (MDT) remains under-explored, particularly in addressing cross-domain word ambiguity. To investigate whether multi-agent...

智能体与工具多语言与语言学OpenReview 项目页
作者位次 #2/11ACL 2026Findings· Findings of ACL

When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM -Powered Safety in Daily Life

Xinyue Lou · Xu Jinan · Jingyi Yin · Xiaolong Wang · Zhaolu Kang · Liaoyouwei · Yixuan Wang · Xiangyu Shi · Fengran Mo · SU Yao · Kaiyu Huang

摘要

As Multimodal Large Language Models (MLLMs) become an indispensable assistant in human life, the unsafe content generated by MLLMs poses a danger to human behavior, perpetually overhanging human society like a sword of Damocles. To investigate and evaluate the safety impact of MLLMs responses on...

多模态评测与基准推理OpenReview 项目页

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