AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment
Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo

TL;DR
AlphaContext is an innovative evolutionary framework that generates high-quality, diverse contexts for creativity assessment, addressing limitations of existing LLM-based generators.
Contribution
It introduces a novel hierarchical, rule-guided, and evolutionary approach combining hypertree planning, MCTS, and MAP-Elites for context generation.
Findings
AlphaContext improves context quality by 8% on average across six metrics.
The method enhances diversity and coherence in generated contexts.
Experiments demonstrate superior performance over existing approaches.
Abstract
Creativity has become a core competence in the era of LLMs and human-AI collaboration, underpinning innovation in real-world problem solving. Crucially, the systematic improvement of creativity necessitates scientifically valid assessment instruments. Psychometric research recognizes context-based assessment as an effective way to measure creative thinking. However, high-quality expert-designed contexts remain scarce. Existing LLM-based generators often struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking. To address these challenges, we propose AlphaContext, an evolutionary tree-based psychometric context generator for creativity assessment. First, the HyperTree Outline Planner formalizes expert-designed outlining as a rule-guided hypertree and performs top-down hierarchical planning. The MCTS-based…
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