HexEval: An Evidence-Driven Hexagonal Framework for Multidimensional Scholar Assessment
Scholar assessment plays a fundamental role in faculty recruitment, funding allocation, academic promotion, and talent discovery. Existing scholar assessment methods predominantly rely on bibliometric indicators and reputation proxies, while recent large language model (LLM)-based approaches mainly focus on evaluating individual research papers rather than comprehensively assessing scholars. We argue that scholar assessment should be formulated as an evidence-driven reasoning problem that jointly considers intrinsic research quality and externally verifiable scholarly behavior. To this end, we propose HexEval, an evidence-driven hexagonal framework for multidimensional scholar assessment....
| Authors | Xiaokang Qu, Yiting Lin |
|---|---|
| Primary category | cs.AI |
| Published | 11 August 2026 |
| Last updated at source | 12 August 2026 |
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