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Is This Idea Novel? An Automated Benchmark for Judgment of Research Ideas

Summary

Deciding whether a research idea is genuinely new usually means reviewing a large body of literature by hand. This is slow and subjective, and it does not scale. LLM-based novelty judges have been proposed, but their evaluations have mostly relied on small, inconsistent human studies, so the approaches are hard to compare. Many of them also give only a binary verdict with no explanation. To address this, the authors build RINoBench, a reproducible benchmark that uses public peer-review data from OpenReview. In the benchmark task, a model receives a structured research idea (problem, objective, solution approach) and the titles and abstracts of related papers. It must assign a novelty score on a five-point rubric and explain the score by comparing the idea with prior work. The authors test several state-of-the-art LLMs, including reasoning models. The models' explanations resemble the arguments human reviewers make, but their scores often differ substantially from the human gold standard. The benchmark gives the field a large-scale, standardized way to measure automated novelty assessment.

Key points

  • Data source: ICLR 2022 and ICLR 2023 submissions and reviews from OpenReview, totalling 6,410 papers with about three reviewers each. Reviewers scored both "Technical" and "Empirical Novelty and Significance."
  • Agreement filter: Papers were dropped if reviewers differed by more than one point within or across the two novelty dimensions. This left 3,535 high-agreement submissions, and the final benchmark contains 1,381 research ideas.
  • Evaluation metrics: Nine automated metrics assess both the rubric-based score (1–5) and the quality of the textual justification.
  • Comparison with prior resources: The only earlier public dataset (Shahid et al., 2025) has 51 binary-labelled ideas and does not evaluate justifications.
  • Baseline setup and availability: Models are prompted zero-shot to return JSON with a 2–4 sentence rationale and an integer novelty score. Data and code are available at github.com/TimSchopf/RINoBench.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4c3gy3f7epnj

@inproceedings{schopf2026is,
  title = {{Is This Idea Novel? An Automated Benchmark for Judgment of Research Ideas}},
  author = {Schopf, Tim and Färber, Michael},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4c3gy3f7epnj},
  url = {https://doi.org/10.63317/4c3gy3f7epnj}
}

References

48 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Osama Mohammed AfzalPreslav Nakov, Tom Hope, Iryna Gurevych, . 2026. Beyond "not novel enough": Enriching scholarly critique with llm-assisted feedback. find
  2. Faez AhmedSharath Kumar Ramachandran, Mark Fuge, Samuel Hunter, Scarlett Miller, . 2018. Interpreting idea maps: Pairwise comparisons reveal what makes ideas novel. Journal of Mechanical Design, 141(2):021102. find
  3. Sam ArtsJianan Hou, Juan Carlos Gomez, . 2021. Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures. Research Policy, 50(2):104144. find
  4. Jinheon BaekSujay Kumar Jauhar, Silviu Cucerzan, Sung Ju Hwang, . 2025. ResearchAgent: Iterative research idea generation over scientific literature with large language models. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguisti… find our card
  5. Joeran BeelMin-Yen Kan, Moritz Baumgart, . 2025. Evaluating sakana’s ai scientist: Bold claims, mixed results, and a promising future? SIGIR Forum, 59(1):1–20. find
  6. Lukas BlecherGuillem Cucurull, Thomas Scialom, Robert Stojnic, . 2024. Nougat: Neural optical understanding for academic documents. In The Twelfth International Conference on Learning Representations. find
  7. Kevin J. BoudreauEva C. Guinan, Karim R. Lakhani, Christoph Riedl, . 2016. Looking across and looking beyond the knowledge frontier: Intellectual distance, novelty, and resource allocation in science. Management Science, 62(10):2765–2783. find
  8. Nicolas BougieNarimasa Watanabe, . 2024. Generative adversarial reviews: When llms become the critic. find
  9. DeepSeek-AIDaya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, and 180 others. 2025. Deepseek-r1: Incentivizing reasoning capabil… find our card
  10. Santo FortunatoCarl T. Bergstrom, Katy Börner, James A. Evans, Dirk Helbing, Staša Milojević, Alexander M. Petersen, Filippo Radicchi, Roberta Sinatra, Brian Uzzi, Alessandro Vespignani, Ludo Waltman, Dashun Wang, Albert-László Barabási, . 2018. Science of science. Science, 359(6379):eaao0185. find
  11. Jacob G. FosterFeng Shi, James Evans, . 2021. Surprise! measuring novelty as expectation violation. find
  12. Juraj GottweisWei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, Khaled Saab, Dan Popovici, Jacob Blum, Fan Zhang, Katherine Chou, Avinatan Hassidim, Burak Gokturk, Amin Vahdat, Pushmeet Kohli, Yossi Matias, and 14… find
  13. Aaron GrattafioriAbhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, a… find
  14. Joshua GuetzkowMichèle Lamont, Grégoire Mallard, . 2004. What is originality in the humanities and the social sciences? American Sociological Review, 69(2):190–212. find
  15. Tarun GuptaDanish Pruthi, . 2025. All that glitters is not novel: Plagiarism in AI generated research. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25721–25738, Vienna, Austria. Association for Computational Linguistics. find
  16. José Manuel Gómez-PérezAndrés García-Silva, Rosemarie Leone, Mirko Albani, Moritz Fontaine, Charles Poncet, Leopold Summerer, Alessandro Donati, Ilaria Roma, Stefano Scaglioni, . 2022. Artificial intelligence and natural language processing and understanding in space: A methodological framework and… find
  17. Jianhua HouDongyi Wang, Jing Li, . 2022. A new method for measuring the originality of academic articles based on knowledge units in semantic networks. Journal of Informetrics, 16(3):101306. find
  18. Rodney KinneyChloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, Jason Dunkelberger, Oren Etzioni, Rob Evans, Sergey Feldman, Joseph Gorney, David Graham, Fangzhou Hu,… find
  19. Long LiWeiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing, . 2025a. Chain of ideas: Revolutionizing research via novel idea development with LLM agents. In Findings of the Association for Com… find
  20. Ruochen LiTeerth Patel, Qingyun Wang, Xinya Du, . 2025b. Mlr-copilot: Autonomous machine learning research based on large language models agents. find
  21. Yan LiuZonglin Yang, Soujanya Poria, Thanh-Son Nguyen, Erik Cambria, . 2025. Harnessing large language models for scientific novelty detection. find
  22. Yang LiuDan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu, . 2023. G-eval: NLG evaluation using gpt-4 with better human alignment. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511–2522, Singapore. Association for Computational Linguis… find
  23. Chris LuCong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha, . 2024. The ai scientist: Towards fully automated open-ended scientific discovery. find our card
  24. Meta. 2025. The llama 4 herd: The beginning of a new era of natively multimodal ai innovation. Accessed: 2025-10-24. find
  25. Sewon MinKalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, Hannaneh Hajishirzi, . 2023. FActScore: Fine-grained atomic evaluation of factual precision in long form text generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Langu… find our card
  26. Sheshera MysoreArman Cohan, Tom Hope, . 2022. Multi-vector models with textual guidance for fine-grained scientific document similarity. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4453–4470,… find
  27. OpenAI:, Sandhini Agarwal, Lama Ahmad, Jason Ai, Sam Altman, Andy Applebaum, Edwin Arbus, Rahul K. Arora, Yu Bai, Bowen Baker, Haiming Bao, Boaz Barak, Ally Bennett, Tyler Bertao, Nivedita Brett, Eugene Brevdo, Greg Brockman, Sebastien Bubeck, Che Chang, and 107 others. 2025. gpt-oss-120b & gpt-oss-… find our card
  28. OpenAI. 2025a. Gpt-5 system card. Accessed: 2025-10-24. find
  29. OpenAI. 2025b. Introducing gpt-4.1 in the api \| openai. Accessed: 2025-10-24. find
  30. OpenAI. 2025c. Openai o3 and o4-mini system card. Accessed: 2025-10-24. find
  31. Cyril PicardKristen M. Edwards, Anna C. Doris, Brandon Man, Giorgio Giannone, Md Ferdous Alam, Faez Ahmed, . 2025. From concept to manufacturing: evaluating vision-language models for engineering design. Artificial Intelligence Review, 58(9):288. find
  32. Marissa RadenskySimra Shahid, Raymond Fok, Pao Siangliulue, Tom Hope, Daniel S. Weld, . 2024. Scideator: Human-llm scientific idea generation grounded in research-paper facet recombination. find
  33. Serhad SaricaJianxi Luo, Kristin L. Wood, . 2020. Technet: Technology semantic network based on patent data. Expert Systems with Applications, 142:112995. find
  34. Simra ShahidMarissa Radensky, Raymond Fok, Pao Siangliulue, Daniel S Weld, Tom Hope, . 2025. Literature-grounded novelty assessment of scientific ideas. In Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025), pages 96–113, Vienna, Austria. Association for Computational Ling… find
  35. Sotaro ShibayamaJian Wang, . 2019. Measuring originality in science. Scientometrics, 122(1):409–427. find
  36. Chenglei SiDiyi Yang, Tatsunori Hashimoto, . 2025. Can LLMs generate novel research ideas? a large-scale human study with 100+ NLP researchers. In The Thirteenth International Conference on Learning Representations. find our card
  37. Haoyang SuRenqi Chen, Shixiang Tang, Zhenfei Yin, Xinzhe Zheng, Jinzhe Li, Biqing Qi, Qi Wu, Hui Li, Wanli Ouyang, Philip Torr, Bowen Zhou, Nanqing Dong, . 2025. Many heads are better than one: Improved scientific idea generation by a LLM-based multi-agent system. In Proceedings of the 63rd Annual M… find
  38. Jiabin TangLianghao Xia, Zhonghang Li, Chao Huang, . 2025. AI-researcher: Autonomous scientific innovation. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. find
  39. Brian UzziSatyam Mukherjee, Michael Stringer, Ben Jones, . 2013. Atypical combinations and scientific impact. Science, 342(6157):468–472. find
  40. Jian WangReinhilde Veugelers, Paula Stephan, . 2017. Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy, 46(8):1416–1436. find
  41. Kai WangBoxiang Dong, Junjie Ma, . 2019. Towards computational assessment of idea novelty. In Proceedings of the 52nd Hawaii International Conference on System Sciences. find
  42. Wenxiao WangLihui Gu, Liye Zhang, Yunxiang Luo, Yi Dai, Chen Shen, Liang Xie, Binbin Lin, Xiaofei He, Jieping Ye, . 2025a. Scipip: An llm-based scientific paper idea proposer. find
  43. Xin WangJiyao Liu, Yulong Xiao, Junzhi Ning, Lihao Liu, Junjun He, Botian Shi, Kaicheng Yu, . 2025b. The-tree: Can tracing historical evolution enhance scientific verification and reasoning? find
  44. Jiaxin WenChenglei Si, Chen Yueh-Han, He He, Shi Feng, . 2025. Predicting empirical AI research outcomes with language models. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. find
  45. Wenqing WuChengzhi Zhang, Tong Bao, Yi Zhao, . 2025a. Sc4anm: Identifying optimal section combinations for automated novelty prediction in academic papers. Expert Systems with Applications, 273:126778. find
  46. Wenqing WuChengzhi Zhang, Yi Zhao, . 2025b. Automated novelty evaluation of academic paper: A collaborative approach integrating human and large language model knowledge. Journal of the Association for Information Science and Technology, 76(11):1452–1469. find
  47. Zonglin YangXinya Du, Junxian Li, Jie Zheng, Soujanya Poria, Erik Cambria, . 2024. Large language models for automated open-domain scientific hypotheses discovery. In Findings of the Association for Computational Linguistics: ACL 2024, pages 13545–13565, Bangkok, Thailand. Association for Computatio… find
  48. Yiming ZhangHarshita Diddee, Susan Holm, Hanchen Liu, Xinyue Liu, Vinay Samuel, Barry Wang, Daphne Ippolito, . 2025. Noveltybench: Evaluating creativity and diversity in language models. In Second Conference on Language Modeling. find

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