OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915737514278912 |
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| author | Zhang, Ming Tan, Kexin Huang, Yueyuan Shen, Yujiong Ma, Chunchun Ju, Li Zhang, Xinran Wang, Yuhui Jing, Wenqing Deng, Jingyi Sha, Huayu Hu, Binze Tong, Jingqi Jiang, Changhao Geng, Yage Ying, Yuankai Zhang, Yue Yin, Zhangyue Xi, Zhiheng Dou, Shihan Gui, Tao Zhang, Qi Huang, Xuanjing |
| author_facet | Zhang, Ming Tan, Kexin Huang, Yueyuan Shen, Yujiong Ma, Chunchun Ju, Li Zhang, Xinran Wang, Yuhui Jing, Wenqing Deng, Jingyi Sha, Huayu Hu, Binze Tong, Jingqi Jiang, Changhao Geng, Yage Ying, Yuankai Zhang, Yue Yin, Zhangyue Xi, Zhiheng Dou, Shihan Gui, Tao Zhang, Qi Huang, Xuanjing |
| contents | Evaluating novelty is critical yet challenging in peer review, as reviewers must assess submissions against a vast, rapidly evolving literature. This report presents OpenNovelty, an LLM-powered agentic system for transparent, evidence-based novelty analysis. The system operates through four phases: (1) extracting the core task and contribution claims to generate retrieval queries; (2) retrieving relevant prior work based on extracted queries via semantic search engine; (3) constructing a hierarchical taxonomy of core-task-related work and performing contribution-level full-text comparisons against each contribution; and (4) synthesizing all analyses into a structured novelty report with explicit citations and evidence snippets. Unlike naive LLM-based approaches, \textsc{OpenNovelty} grounds all assessments in retrieved real papers, ensuring verifiable judgments. We deploy our system on 500+ ICLR 2026 submissions with all reports publicly available on our website, and preliminary analysis suggests it can identify relevant prior work, including closely related papers that authors may overlook. OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01576 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment Zhang, Ming Tan, Kexin Huang, Yueyuan Shen, Yujiong Ma, Chunchun Ju, Li Zhang, Xinran Wang, Yuhui Jing, Wenqing Deng, Jingyi Sha, Huayu Hu, Binze Tong, Jingqi Jiang, Changhao Geng, Yage Ying, Yuankai Zhang, Yue Yin, Zhangyue Xi, Zhiheng Dou, Shihan Gui, Tao Zhang, Qi Huang, Xuanjing Information Retrieval Artificial Intelligence Computation and Language Evaluating novelty is critical yet challenging in peer review, as reviewers must assess submissions against a vast, rapidly evolving literature. This report presents OpenNovelty, an LLM-powered agentic system for transparent, evidence-based novelty analysis. The system operates through four phases: (1) extracting the core task and contribution claims to generate retrieval queries; (2) retrieving relevant prior work based on extracted queries via semantic search engine; (3) constructing a hierarchical taxonomy of core-task-related work and performing contribution-level full-text comparisons against each contribution; and (4) synthesizing all analyses into a structured novelty report with explicit citations and evidence snippets. Unlike naive LLM-based approaches, \textsc{OpenNovelty} grounds all assessments in retrieved real papers, ensuring verifiable judgments. We deploy our system on 500+ ICLR 2026 submissions with all reports publicly available on our website, and preliminary analysis suggests it can identify relevant prior work, including closely related papers that authors may overlook. OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review. |
| title | OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment |
| topic | Information Retrieval Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2601.01576 |