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
摘要
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 Alpha-Context, 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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper2
- DEFINED: A Data-Efficient Computational Framework for Fine-Grained Creativity Assessment in Debate ScenariosTongzhou Yu, Mingjia Li, Hong Qian, Wenkai Wang 等KDD 2026 · 被引用 1 次
- CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive EngagementHong Qian, Yuanhao Liu, Zihan Zhou, Zongbao Zhang 等ICML 2026
它引用的顶会 Paper6
- DOC: Improving Long Story Coherence With Detailed Outline ControlKevin Yang, Dan Klein, Nanyun Peng, Yuandong TianACL 2023 · 被引用 23 次
- SS-GEN: A Social Story Generation Framework with Large Language ModelsYi Feng, Mingyang Song, Jiaqi Wang, Zhuang Chen 等AAAI 2025 · 被引用 6 次
- Collective Critics for Creative Story GenerationMinwook Bae, Hyounghun KimEMNLP 2024 · 被引用 3 次
- LongWriter: Unleashing 10, 000+ Word Generation from Long Context LLMsYushi Bai, Jiajie Zhang, Xin Lv, Linzhi Zheng 等ICLR 2025
- Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP ResearchersChenglei Si, Diyi Yang, Tatsunori HashimotoICLR 2025
相关 Paper
- DEPT: Large Language Model–Driven Automated Algorithm Design via Evolutionary Program TreesBin Chen, Shouliang Zhu, Beidan Liu, Yong Zhao 等ICML 2026 · 被引用 3 次
- AMACE: Automatic Multi-Agent Chart Evolution for Iteratively Tailored Chart GenerationHyuk Namgoong, Jeesu Jung, Hyeonseok Kang, Yohan Lee 等EMNLP 2025
- EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific DiscoveryXiaoyu Xiong, Yuqi Ren, Deyi XiongACL 2026 · 被引用 1 次
- Agentic Context Engineering: Evolving Contexts for Self-Improving Language ModelsQizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma 等ICLR 2026 · 被引用 374 次
- Meta Context Engineering via Agentic Skill EvolutionHaoran Ye, Xuning He, Vincent Arak, Haonan Dong 等ICML 2026 · 被引用 30 次
