arXiv AI By Samuel Schapiro, Core Francisco Park, Felix Sosa, Lav R. Varshney

CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse

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arXiv:2607. 01433v1 Announce Type: new Abstract: Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended questions, in what has been termed the artificial hivemind effect.

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arXiv AI
Aug 11

Large Language Models Align with the Human Brain during Creative Thinking

arXiv:2604. 03480v2 Announce Type: replace-cross Abstract: Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engine.

By Mete Ismayilzada, Simone A. Luchini, Abdulkadir Gokce, Badr AlKhamissi, Antoine Bosselut, Antonio Laverghetta Jr., Lonneke van der Plas, Roger E. Beaty
arXiv Computation and Language
Sep 4

Decoupled Analysis-Judging: An Automated Creativity Evaluator Using LLMs in Complex Multi-step Creativity Tasks

The paper introduces CreaEval, an automated creativity evaluator designed for complex multi-step tasks (CGPST). It separates the evaluation process into two phases: a memory‑augmented analysis that transforms responses into structured evidence, and an evidence‑based judging step that scores without seeing raw outputs. Experiments show CreaEval outperforms existing baselines by an average of 22.74% across CGPST and two simpler creativity tasks.

By Xiangyu Wang, Jin Wu, Xiaoyu Li, Chanjin Zheng, Yifeng Zhou