arXiv AI July 21, 2026 By Makoto Sato

ReMIND: Orchestrating Modular Large Language Models for Controllable Serendipity A REM-Inspired System Design for Emergent Creative Ideation

arXiv:2601. 07121v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used not only for problem solving but also for creative ideation; however, generating ideas that are both novel and coherent remains challenging.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 11

Automated Creativity Evaluation of Language Models Across Open-Ended Tasks

arXiv:2606. 11762v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential.

By Min Sen Tan, Zachary Kit Chun Choy, Syed Ali Redha Alsagoff, Nadya Yuki Wangsajaya, Mohor Banerjee, Swaagat Bikash Saikia, Alvin Chan

llms rag agents benchmarks

Hugging Face Trending Papers

Jun 10

Automated Creativity Evaluation of Language Models Across Open-Ended Tasks

Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks.

llms rag agents benchmarks

arXiv AI

Jul 3

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

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.

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

llms fine-tuning

arXiv AI

Jun 16

LLM Jaggedness Unlocks Scientific Creativity

arXiv:2605. 10574v3 Announce Type: replace Abstract: As artificial intelligence advances, models are not improving uniformly.

By Shray Mathur, J. Anibal Boscoboinik, Esther H. R. Tsai, Kevin G. Yager

llms benchmarks

arXiv AI

Aug 10

CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity

arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.

By Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin

llms reinforcement-learning safety

arXiv AI

Aug 5

Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models

arXiv:2603. 19087v2 Announce Type: replace Abstract: Creativity is the ability to come up with novel ideas, a capacity crucial for human development and flourishing.

By Qiawen Ella Liu, Marina Dubova, Henry Conklin, Takumi Harada, Thomas L. Griffiths

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Automated Creativity Evaluation of Language Models Across Open-Ended Tasks

Automated Creativity Evaluation of Language Models Across Open-Ended Tasks

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

LLM Jaggedness Unlocks Scientific Creativity

CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity

Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models