🏢 Works of Xiaohongshu (Little Red Book) from 2026

12 works · the most cited first · ← Xiaohongshu (Little Red Book)

1. LoopTool: Closing the Data–Training Loop for Robust LLM Tool CallsAnnual Meeting of the Association for Computational Linguistics (ACL)Kangning Zhang, Weiwen Liu, Wenxiang Jiao, et al.2026 · 6 citations
2. LLM-Powered Benchmark Factory: Reliable, Generic, and EfficientAnnual Meeting of the Association for Computational Linguistics (ACL)Peiwen Yuan, Shaoxiong Feng, Yiwei Li, et al.2026 · 3 citations
3. One Token Is Enough: Improving Diffusion Language Models with a Sink TokenFindings of the Association for Computational LinguisticsZihou Zhang, Zheyong Xie, Li Zhong, et al.2026 · 2 citations
4. RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking ServicesAnnual Meeting of the Association for Computational Linguistics (ACL)Fei zhao, Chonggang Lu, Haofu Qian, et al.2026 · 2 citations
5. Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Wei Wu, Liyi Chen, Congxi Xiao, et al.2026 · 1 citation
6. Robust Tool Use via Fission-GRPO: Learning to Recover from Execution ErrorsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiwei Zhang, Fei Zhao, Rui Wang, et al.2026 · 1 citation
7. SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data UtilityAnnual Meeting of the Association for Computational Linguistics (ACL)Xuyang Zhi, Peilun Zhou, Chengqiang Lu, et al.2026 · 1 citation
8. Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language ModelsFindings of the Association for Computational LinguisticsWenxuan Wang, Yuk-Kit Chan, Zixuan Ling, et al.2026
9. Leveraging Outline-Optimized Generative Interactions and Critique for Self-Refining Outlines with Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Hengwei Liu, Haoyuan Ma, Qingqing Lyu, et al.2026
10. MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement LearningFindings of the Association for Computational LinguisticsQianhao Yuan, Jie Lou, Zichao Li, et al.2026
11. Pause or Fabricate? Training Language Models for Grounded ReasoningFindings of the Association for Computational LinguisticsYiwen Qiu, Linjuan Wu, Yizhou Liu, et al.2026
12. R\textthreesuperiorA: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content PlatformsAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaowei Yuan, Lei Jin, Haoxin Zhang, et al.2026

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