🔗 Who cites «Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025»

17 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · ← back to «Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025»

1. MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning ResearchConference on Neural Information Processing Systems (NeurIPS)Hui Chen, Miao Xiong, Yujie Lu, et al.2025 · 21 citations
2. AI for Auto-Research: Roadmap & User GuidearXiv:2605.18661Lingdong Kong, Xian Sun, Wei Chow, et al.2026 · 5 citations
3. SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones?arXiv:2605.30329Sy-Tuyen Ho, Minghui Liu, Huy Nghiem, et al.2026 · 2 citations
4. "Give a Positive Review Only": An Early Investigation Into In-Paper Prompt Injection Attacks and Defenses for AI ReviewersarXiv:2511.01287Qin Zhou, Zhexin Zhang, Zhi Li, et al.2025 · 2 citations
5. Exploring the use of AI authors and reviewers at Agents4SciencearXiv:2511.15534Federico Bianchi, Owen Queen, Nitya Thakkar, et al.2025 · 2 citations
6. PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in OverleafAnnual Meeting of the Association for Computational Linguistics (ACL)Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner, et al.2026 · 1 citation
7. From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper EvaluationFindings of the Association for Computational LinguisticsPujun Zheng, Jiacheng Yao, Jinquan Zheng, et al.2026
8. Preference Optimization for Review Question Generation Improves Writing QualityFindings of the Association for Computational LinguisticsKarun Sharma, Vidushee Vats, Shengzhi LI, et al.2026
9. When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and MitigationarXiv:2609.20942Sy-Tuyen Ho, Minghui Liu, Furong Huang2026

8 more papers of our library cite it; they have no page here yet.

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