--- title: "Xiaohongshu (Little Red Book) — works of 2026 — 12 papers" description: "12 works of Xiaohongshu (Little Red Book) from 2026 in our library, the most cited first. Among them: LoopTool: Closing the Data–Training…" canonical: "https://research.elamur.ai/free/en/companies/xiaohongshu-little-red-book/year/2026/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/companies/xiaohongshu-little-red-book/year/2026/) [home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [companies](https://md.research.elamur.ai/free/en/companies/) / [Xiaohongshu (Little Red Book)](https://md.research.elamur.ai/free/en/companies/xiaohongshu-little-red-book/) / 2026 # Works of Xiaohongshu (Little Red Book) from 2026 12 works · the most cited first · [← Xiaohongshu (Little Red Book)](https://md.research.elamur.ai/free/en/companies/xiaohongshu-little-red-book/) 1. **[LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls](https://md.research.elamur.ai/free/en/papers/zhang-2026-looptool-closing-data-training-loop/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kangning Zhang, Weiwen Liu, Wenxiang Jiao, et al.*2026 · 6 citations* 2. **[LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient](https://md.research.elamur.ai/free/en/papers/yuan-2026-llm-powered-benchmark-factory-reliable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peiwen Yuan, Shaoxiong Feng, Yiwei Li, et al.*2026 · 3 citations* 3. **[One Token Is Enough: Improving Diffusion Language Models with a Sink Token](https://md.research.elamur.ai/free/en/papers/zhang-2026-one-token-is-enough-improving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihou Zhang, Zheyong Xie, Li Zhong, et al.*2026 · 2 citations* 4. **[RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services](https://md.research.elamur.ai/free/en/papers/zhao-2026-redone-2-0-rethinking-domain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fei zhao, Chonggang Lu, Haofu Qian, et al.*2026 · 2 citations* 5. **[Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models](https://md.research.elamur.ai/free/en/papers/wu-2026-anti-length-shift-dynamic-outlier/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Wu, Liyi Chen, Congxi Xiao, et al.*2026 · 1 citation* 6. **[Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors](https://md.research.elamur.ai/free/en/papers/zhang-2026-robust-tool-use-fission-grpo/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Utility](https://md.research.elamur.ai/free/en/papers/zhi-2026-spard-self-paced-curriculum-rl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Models](https://md.research.elamur.ai/free/en/papers/wang-2026-identifying-achilles-heel-iterative-method/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, et al.*2026* 9. **[Leveraging Outline-Optimized Generative Interactions and Critique for Self-Refining Outlines with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/liu-2026-leveraging-outline-optimized-generative-interactions/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hengwei Liu, Haoyuan Ma, Qingqing Lyu, et al.*2026* 10. **[MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yuan-2026-memsearcher-iterative-memory-integration-search/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qianhao Yuan, Jie Lou, Zichao Li, et al.*2026* 11. **[Pause or Fabricate? Training Language Models for Grounded Reasoning](https://md.research.elamur.ai/free/en/papers/qiu-2026-pause-or-fabricate-training-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiwen Qiu, Linjuan Wu, Yizhou Liu, et al.*2026* 12. **[R\textthreesuperiorA: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms](https://md.research.elamur.ai/free/en/papers/yuan-2026-r-textthreesuperiora-reinforced-reasoning-relevance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaowei Yuan, Lei Jin, Haoxin Zhang, et al.*2026* - [← back to](https://md.research.elamur.ai/free/en/companies/xiaohongshu-little-red-book/) — Xiaohongshu (Little Red Book)