AI RESEARCH
SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models
arXiv CS.CL
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ArXi:2605.19357v1 Announce Type: new Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice. Most benchmarks are manually curated or domain-generic, limiting scalability and alignment with real scientific use cases. In this paper, we propose a new framework named SciCustom to address the problem. It enables the custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.