arXiv:2606.18936v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly embedded in AI for Science (AI4Science) workflows, from scientific question answering and literature analysis to laboratory planning and autonomous discovery. This progress creates an urgent need for safety benchmarks that evaluate not only scientific competence, but also whether models recognize and avoid risks in high-stakes scientific contexts. Existing AI4Science safety datasets cover several disciplines and task formats, leaving the underlying risk dimensions underspecified. We introduce \textbf{
The increasing integration of LLMs into high-stakes scientific research necessitates specialized benchmarks to ensure safety and ethical deployment.
A strategic reader should care because unchecked AI in science could lead to significant unintended consequences, impacting research integrity, public safety, and potentially the future of scientific discovery.
The introduction of a risk-dimension-aware benchmark shifts the focus from mere competence to the crucial evaluation of safety and risk avoidance in AI for Science applications.
- · AI safety researchers
- · Scientific institutions
- · Regulatory bodies
- · Ethical AI developers
- · Developers prioritizing speed over safety
- · Unregulated AI4Science platforms
Improved safety and reliability of AI applications in scientific research.
Increased trust in AI-driven scientific discoveries and a potential acceleration of responsible innovation.
The establishment of global standards and regulations for AI ethics in scientific and high-stakes computational fields.
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Read at arXiv cs.AI