Expert-validated STEM QA
Recent advancements in AI are helping scientists achieve breakthroughs in fields such as mathematics, medicine, and materials sciences. New evaluation datasets for AI models contribute to such advancement in AI. In the STEM domain, frontier models have consumed most of the available online data, creating the need for human-created datasets that codify the knowledge of leading experts in the domain. There are several STEM datasets available for the research community in this field. However, there are some gaps in these datasets, leaving room for improvement. Examples of gaps include (1) saturation in model performance on these datasets, leaving no head-room for meaningful evaluations, (2) ...
| Authors | Kihwan Han, Saurabh Patil, Chinmayee Shukla et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 June 2026 |
| Last updated at source | 6 June 2026 |
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