arXiv:2606.12071v1 Announce Type: cross Abstract: LLMs are increasingly used to generate and judge scientific ideas. This makes novelty evaluation a central problem. Full idea evaluation is difficult because it often requires judging a method, its feasibility, and its empirical promise. We therefore study a cleaner upstream object: the research question (RQ). RQ generation is a prerequisite for scientific ideation, and RQs can be compared against questions pursued in real papers. We introduce RQ-Bench, a benchmark built from recent arXiv papers. For each paper, we reconstruct author-anchored R
The increasing deployment of LLMs for scientific ideation and review necessitates a robust understanding of their limitations in critical tasks like novelty assessment.
Reliable novelty evaluation is fundamental to scientific progress and the integrity of research, making LLM performance in this area a key concern for AI integration in science.
This research provides a benchmark and highlights the limitations of LLMs as judges for scientific novelty, advocating for careful consideration as these tools become more prevalent in research.
- · Researchers developing better novelty assessment AI
- · Human expert reviewers
- · Academic publishing platforms
- · Uncritically deployed LLM-as-judge systems
- · Automated research question generators without human oversight
The adoption of LLMs for scientific novelty assessment will be slowed or refined based on these findings.
New AI models specifically designed for nuanced scientific evaluation, rather than general judgment, will emerge.
The definition of 'novelty' in scientific research may evolve to accommodate both human and AI analytical capabilities.
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Read at arXiv cs.AI