arXiv:2605.10574v2 Announce Type: replace Abstract: As artificial intelligence advances, models are not improving uniformly. Instead, progress unfolds in a jagged fashion, with capabilities growing unevenly across tasks, domains, and model scales. In this work, we examine this dynamic jaggedness through the lens of scientific idea generation. We introduce SciAidanBench, a benchmark of open-ended scientific questions designed to measure the scientific creativity of large language models (LLMs). Given a scientific question, models are asked to generate as many unique and coherent ideas as possib
The rapid advancement of large language models is leading to a deeper understanding of their non-uniform capabilities and their potential for complex cognitive tasks like scientific creativity.
This research provides a new benchmark for evaluating LLMs on open-ended scientific idea generation, moving beyond mere task completion to assess higher-order thinking.
The focus shifts from simply optimizing LLM performance to understanding and leveraging their 'jaggedness' – their uneven capabilities – for areas requiring creativity and novel idea generation.
- · AI researchers and developers
- · Scientific research institutions
- · LLM providers
- · Innovation-driven companies
- · Traditional scientific idea generation methods (potentially, long-term)
- · LLMs with undifferentiated capabilities
New benchmarks and methodologies will emerge for evaluating 'jagged' AI system capabilities.
LLMs specifically designed or fine-tuned for scientific discovery and creative problem-solving will gain prominence.
The definition of 'scientific creativity' may be expanded or re-evaluated in the context of AI contributions.
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Read at arXiv cs.AI