@inproceedings{wang-etal-2026-alphacontext,
    title = "{A}lpha{C}ontext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment",
    author = "Wang, Yixuan  and
      Huang, Yue  and
      Qian, Hong  and
      Wei, Yunzhao  and
      Ding, Yifei  and
      Wang, Wenkai  and
      Liu, Zhi  and
      Huang, Zhongjing  and
      Zhou, Aimin  and
      Guo, Jiajun",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1261/",
    pages = "27336--27357",
    ISBN = "979-8-89176-390-6",
    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 Context Generator fills the outline via MCTS to balance global structure and local quality. Then, the Evolutionary Context Optimizer evolves contexts with MAP-Elites by repeatedly updating niche elites to jointly improve diversity and quality. Finally, the Assessment-Guided Evolution Refiner simulates virtual participants with diverse styles and recycles weak contexts for further evolution. Experiments show that AlphaContext yields an average improvement of 8{\%} over competitive methods across 6 quality metrics."
}