用历史记忆+细粒度分析，自动判断论文创新性。
MemoNoveltyAgent: A Historical Research Memory-Aware Agent Workflow for Paper Novelty Assessment
- 构建学术史记忆树，理解领域演化脉络。
- 分解论文为创新点，自验证提升报告可信度。
- 适合需要深度评审的科研人员或期刊审稿人。
为减轻论文筛选负担，研究者越来越多依赖AI代理进行论文评估与创新性判断。然而，现有AI代理缺乏处理学术文献的专门机制，分析结果常流于表面且质量不足。为此，我们提出MemoNoveltyAgent，一种多智能体系统，旨在生成全面且忠实的创新性报告。该系统不仅通过RAG检索具体前序论文证据，还基于大规模学术语料构建高层抽象记忆，将研究组织为分层树结构，提炼领域特有的演化轨迹，提供更广阔的历时背景。此外，我们将论文分解为离散的创新点进行细粒度分析与检索，并引入自验证机制以提升报告的忠实度。针对此类开放式生成任务的评估难题，我们提出一种增强型检查清单评估方法，结合RAG实现可靠且有据可依的评价。大量实验表明，MemoNoveltyAgent在创新性评估上优于GPT-5 DeepResearch 13.69%。代码与演示已公开于https://github.com/SStan1/MemoNoveltyAgent。
原文摘要 · Abstract (English)
To alleviate the heavy burden of paper screening, researchers increasingly rely on existing AI agents, such as AI reviewers or DeepResearch, for paper evaluation and novelty assessment. However, lacking specialized mechanisms for processing scholarly literature, their analyses often produce superficial results with noticeable deficiencies in quality. To bridge this gap, we introduce MemoNoveltyAgent, a multi-agent system designed to generate comprehensive and faithful novelty reports. Beyond retrieving concrete prior-paper evidence via RAG, our system incorporates a high-level abstract memory constructed from large-scale scholarly corpora. This memory organizes research into hierarchical trees to distill field-specific evolutionary trajectories, thereby providing a broader historical context. Furthermore, we decompose papers into discrete novelty points for fine-grained analysis and retrieval, while employing a self-validation mechanism to improve report faithfulness. Finally, to address the evaluation challenges of such open-ended generation tasks, we propose a RAG-augmented checklist evaluation method that enables reliable and evidence-grounded assessments. Extensive experiments demonstrate that MemoNoveltyAgent outperforms GPT-5 DeepResearch by 13.69%. Code and demo are available at https://github.com/SStan1/MemoNoveltyAgent
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