NoveltyScope: A Multi-Agent Research Assistant for Evidence-Grounded Novelty Assessment of Scientific Papers
NoveltyScope is a prototype that compares a scientific paper with retrieved prior work and produces a structured report and a five-level novelty score.
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Key points
- The system extracts the core task and at most two author-claimed contributions, then searches a closed local corpus using BM25 and dense retrieval with reciprocal rank fusion.
- Contribution comparisons request paired passages for potential refutations, and quotations are checked against cached source text.
- A HiCLIP case study illustrates the contribution comparisons and paired evidence passages.
- Overall, the results demonstrate the feasibility of evidence-grounded novelty assessment and show that the multi-agent configurations achieve lower scoring errors than their single-LLM counterparts across both evaluated models.
Sources (1)
- [1]NoveltyScope: A Multi-Agent Research Assistant for Evidence-Grounded Novelty Assessment of Scientific PapersarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 10:09 PM
NoveltyScope is a prototype that compares a scientific paper with retrieved prior work and produces a structured report and a five-level novelty score.
The system extracts the core task and at most two author-claimed contributions, then searches a closed local corpus using BM25 and dense retrieval with reciprocal rank fusion.
Extractive summary: sentences quoted from the sources.
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