NoveltyScope: A Multi-Agent Research Assistant for Evidence-Grounded Novelty Assessment of Scientific Papers
Read the original on arXiv AI →NoveltyScope is a prototype that evaluates the novelty of a scientific paper by comparing it with prior work and generating a structured report and a five‑level novelty score. It extracts the core task and up to two author‑claimed contributions, retrieves relevant passages from a local corpus, and uses a four‑role multi‑agent system (Prosecutor, Defender, Judge, Critic) to assess novelty, allowing a single revision of the Judge’s score. Evaluation on 50 RINoBench samples shows that multi‑agent scoring with Gemini or Qwen3 achieves lower mean absolute errors (1.26–1.16) than single‑LLM scoring (1.48–1.22) and outperforms a vector‑similarity baseline (1.68).
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