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NoveltyAgent: Autonomous Novelty Reporting Agent with Point-wise Novelty Analysis and Self-Validation

Jiajun Hou, Hexuan Deng, Wenxiang Jiao, Xuebo Liu, Xiaopeng Ke, Min Zhang

Abstract

The exponential growth of academic publications has led to a surge in papers of varying quality, increasing the cost of paper screening. Current approaches either use novelty assessment within general AI Reviewers or repurpose DeepResearch, which lacks domain-specific mechanisms and thus delivers lower-quality results. To bridge this gap, we introduce NoveltyAgent, a multi-agent system designed to generate comprehensive and faithful novelty reports, enabling thorough evaluation of a paper's originality. It decomposes manuscripts into discrete novelty points for fine-grained retrieval and comparison, and builds a comprehensive related-paper database while cross-referencing claims to ensure faithfulness. Furthermore, to address the challenge of evaluating such open-ended generation tasks, we propose a checklist-based evaluation framework, providing an unbiased paradigm for building reliable evaluations. Extensive experiments show that NoveltyAgent achieves state-of-the-art performance, outperforming GPT-5 DeepResearch by 10.15%. We hope this system will provide reliable, high-quality novelty analysis and help researchers quickly identify novel papers. Code and demo are available at https://github.com/SStan1/NoveltyAgent.

NoveltyAgent: Autonomous Novelty Reporting Agent with Point-wise Novelty Analysis and Self-Validation

Abstract

The exponential growth of academic publications has led to a surge in papers of varying quality, increasing the cost of paper screening. Current approaches either use novelty assessment within general AI Reviewers or repurpose DeepResearch, which lacks domain-specific mechanisms and thus delivers lower-quality results. To bridge this gap, we introduce NoveltyAgent, a multi-agent system designed to generate comprehensive and faithful novelty reports, enabling thorough evaluation of a paper's originality. It decomposes manuscripts into discrete novelty points for fine-grained retrieval and comparison, and builds a comprehensive related-paper database while cross-referencing claims to ensure faithfulness. Furthermore, to address the challenge of evaluating such open-ended generation tasks, we propose a checklist-based evaluation framework, providing an unbiased paradigm for building reliable evaluations. Extensive experiments show that NoveltyAgent achieves state-of-the-art performance, outperforming GPT-5 DeepResearch by 10.15%. We hope this system will provide reliable, high-quality novelty analysis and help researchers quickly identify novel papers. Code and demo are available at https://github.com/SStan1/NoveltyAgent.
Paper Structure (49 sections, 28 figures, 6 tables)

This paper contains 49 sections, 28 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. DeepResearch.
  4. AI Reviewer.
  5. NoveltyAgent Framework
  6. Literature Database Construction
  7. Database Collection.
  8. Database Filtering.
  9. Point-Wise Report Generation
  10. Splitting Agent.
  11. Analyst Agent.
  12. Information Provider Agent.
  13. Summarizer Agent.
  14. Faithfulness-Enhanced Self-Validation
  15. Validator Agent.
  16. ...and 34 more sections

Figures (28)

  • Figure 1: Frameworks of existing methods and the proposed NoveltyAgent for novelty analysis.
  • Figure 2: The NoveltyAgent workflow. The framework first constructs a citation-based full-text database from the input paper. The Splitting Agent and Analyst jointly decompose the paper into discrete novelty points and perform RAG-based novelty analysis for each. The Summarizer synthesizes these findings into a structured report. Finally, the Validator and Improver conduct self-validation by cross-referencing claims against source texts, while also polishing the report to improve readability and fluency.
  • Figure 3: Example of the Paper Content Summary section in a generated novelty report.
  • Figure 4: Example of the Point-wise Novelty Analysis section, showing the comparison structure for one novelty point.
  • Figure 5: Example of the Novelty Summary section, including overall assessment, limitations, and final score.
  • ...and 23 more figures