Creativity in Education and Neuroscience

Derniers papiers

• Reflecting on Creative-Boundaries with an AI Co-Doodler

  Samia Menon, Samyukta Jayaram, Chetan Goenka, Shm Garanganao Almeda · 6 octobre 2026

  In this pictorial, we consider how the negotiation of creative boundaries with a co-creative AI system can create moments for personal creative reflection. We ground this in our experiences with Froggi-Draw, a single-initiative co-doodling system that gives users power to decide when and how much an…

• CreativeFlow: A One-to-Many Analogical Relation Transfer Method for 3D Asset Generation

  Xuechen Li, Shuai Zhang, Nanxuan Zhao, Qing Chen · 6 octobre 2026

  Inspired by cognitive science, we present CREATIVEFLOW, an analogical generation framework that explicitly models analogical divergent thinking to mitigate creative homogenization in text-to-3D pipelines. Our method derives a series of meaningful yet relationally similar source-target asset pairs, e…

• Verifiable, Articulable, and Tacit Components of Preference

  Alexander Spangher, Sheldon Huang, Andreas Haupt, Noah D. Goodman, Diyi Yang, Daniel E. Ho, Sanmi Koyejo · 5 octobre 2026

  What makes a short story gripping; a news article newsworthy; or a math proof elegant? These constructs resist articulation or verification; their meaning is at least partially tacit. However, modern AI models are improved primarily via articulated constitutions, rubrics and verifiers (i.e. in RLAIF…

• The Effects of Incremental Instruction Delivery on Language-Model Creative Writing

  Anshuman Singh, Abrar Eyasir, Haseeb Yaqoob, John Manavalan · 30 septembre 2026

  Large language models are increasingly used as interactive writing tools, where users develop stories, revise ideas, and introduce new requirements across multiple turns rather than specifying a complete brief upfront. Yet most evidence on multi-turn instruction degradation comes from tasks with obj…

• The Judge Is Not Its Twin: Post-training makes a model's writing more predictable but barely moves its taste, as a judge, toward predictable writing

  Arman Nik Khah, Arvin Bahreini · 30 septembre 2026

  Language models are now routinely graded by other language models. If post-training makes a model's own writing more predictable, it may also teach the same model, acting as a judge, to reward predictable writing, so that progress on creativity would be invisible to automated evaluation. We follow t…

• Reinforcing Agentic Creativity in Scientific Ideation with Night Science

  Priyanka Kargupta, Silviu Cucerzan, Shweti Mahajan, Allen Herring, Jiawei Han, Ryen W. White, Sujay Kumar Jauhar · 29 septembre 2026

  Large language models (LLMs) excel at structured, verifiable tasks, but their low-entropy bias can produce homogeneous and predictable outputs, limiting their utility for open-ended scientific ideation. Effective discovery, however, spans a broader creative spectrum: from structured day science to l…

• PainterBench: A Figural Divergent-Thinking Benchmark for Tool-Using Language Models

  Shane K. A. Dalumura Hettige, Jonas Oppenlaender · 29 septembre 2026

  Figural divergent thinking is the ability to develop a given shape fragment into an original drawing. In humans, this ability is assessed with incomplete-drawing tasks. We introduce PainterBench, a benchmark that ports the incomplete-drawing task to the agentic setting. The agent draws on a canvas t…

• Measuring the Creativity of Frontier LLMs in Automated Research

  Yiheng Zhao, Mengzhuo Chen, Chengming Hu, Pengyi Liao, Yihan Huang, Yiran Pang · 23 septembre 2026

  Frontier LLMs are increasingly capable of conducting automated research, yet their creativity in this setting has not been systematically evaluated. We propose a set of metrics to evaluate creativity along the two dimensions of valueness and novelty. Valueness assesses whether each proposed idea is …

• Is Imagination Derived from Hallucination? A Cross-Taxonomy Evaluation of Imagination and Hallucination in Large Language Models

  Zixuan Tang, Hongzong Li, Shuxin Zhuang, Dapeng Wu, Zi Liang · 22 septembre 2026

  Imagination performs as a high-level function of large language models (LLMs) which determines the potential of how an LLM creates unseen or creative content. While existing works have built a rich family of creativity benchmarks for this ability, they only measure how far an output departs from com…

• What Makes Creation Human? Authorship, Reasons, and Meaningful Human Control in Generative AI

  Yuxi Cao · 11 septembre 2026

  Generative artificial intelligence (GenAI) significantly expands creators' productive capacity, but this does not necessarily entail a corresponding increase in creative agency or authorship. This paper distinguishes creativity at the level of the work from creative agency at the level of the creato…

• Human-AI Co-Creativity: Advances, Opportunities, and Challenges

  Adish Singla, Abhilasha Ravichander, Liwei Jiang, Alexander Spangher, Alice Oh, Jiho Jin, Jun Seong Kim, Changyoon Lee, Manh Hung Nguyen, Chao Wen · 9 septembre 2026

  This survey article has grown out of the human-AI co-creativity workshop organized by the authors at the ICML 2026 conference. We organized this workshop as part of a community-building effort to bring together researchers and practitioners interested in topics of generative AI, creativity, and huma…

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

  Xiangyu Wang, Jin Wu, Xiaoyu Li, Chanjin Zheng, Yifeng Zhou · 4 septembre 2026

  Automated evaluation of creativity tasks remains challenging for LLM-as-a-Judge, as LLM is susceptible to biases such as verbosity bias and leniency bias. Such limitations are particularly evident in Contextually-Grounded and Procedurally-Structured Tasks (CGPST), a complex multi-step creativity tas…

• Collective creativity in hybrid societies

  Mason Youngblood, Katie Mudd, Manuel Anglada-Tort, Cameron Jones, Elena Miu, Diana Omigie, Margaret Schedel · 3 septembre 2026

  Generative AI is changing how cultural artifacts are created and circulated, and with it our understanding of creativity itself. Researchers disagree about whether these tools enrich or impoverish culture, and we argue that much of that disagreement comes from conflating two distinct components of c…

• Creative Generation via Multi-Agent Debate: Does Debate Suppress Diversity?

  Tien Anh Nguyen, Khanh-Binh Nguyen, Van Dai Do, Svetha Venkatesh, Hung Le · 2 septembre 2026

  Creative generation tasks, such as narrative writing and scientific ideation, demand both high-quality outputs and distinct responses across independent runs to maximize exploration. Multi-Agent Debate (MAD) has shown strong quality gains on factual and reasoning tasks, making it a natural candidate…

• CLIN: an Objective Framework for Evaluating Creativity in Short Persian Literary Text

  Mohammad Reza Modarres, Armin Tourajmehr, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar · 1 septembre 2026

  Evaluating creativity in large language model (LLM) outputs remains challenging because creativity is multidimensional and human-centered. We examine how reliably LLMs evaluate short literary text in Persian, a low-resource language, across multiple evaluation strategies and prompt formulations. We …

• Using Poly-Encoders for Computationally Efficient Automated Creativity Assessment

  Sam Grouchnikov, Phillip Gregory, Jiho Noh · 28 août 2026

  Automated creativity assessment has been a long standing challenge, with traditional methods often being resource intensive or lacking practical accuracy. We introduce a novel approach by using Poly-Encoder for computationally efficient and accurate automated creativity assessment. We fine-tuned a P…

• Artificial Intelligence Models Can Predict and Collaboratively Modulate Human Memory Search

  Eric Lacosse, Mariana Duarte, Graham Todd, Peter M. Todd, Daniel C. McNamee · 28 août 2026

  Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many language-based tasks, for example routine coding, these artificial intelligence models have reduced, or even eliminated, the need for human input. But rath…

• The Limits of Automatic Evaluation of Creativity in Large Language Models

  Alessandro Tutone, Giorgio Franceschelli, Mirco Musolesi · 26 août 2026

  Large Language Models (LLMs) are increasingly capable of generating text that challenges human performance in domains requiring creativity, yet evaluating creativity in LLM-generated content remains a significant challenge. Here, we investigate whether current automatic evaluation methods can reliab…

• Are LLMs becoming similarly creative? Evidence from three years of models

  Nirav Patel, Josiah Crossman, Eva Aggarwal, Emily Wenger · 21 août 2026

  Many benchmarks track Large Language Model (LLM) performance on tasks with verifiable answers, but less is known about how LLM performance is evolving on open-ended tasks, where creativity, originality and diversity may matter as much as quality. As LLMs increasingly support human ideation and creat…

• Harnessing Abundance: A Generativity Perspective on Human-GenAI Collaboration

  Yoram M Kalman, Yun Wan · 11 août 2026

  Research on human-GenAI collaboration yields conflicting findings: GenAI can enhance creativity yet reduce collective diversity, with uneven benefits across skill levels. Rather than treating these as contradictions, we argue they reflect a core feature of GenAI: abundance. GenAI makes ideas, drafts…

• AI-AI co-creation outperforms human pairs in creative tasks

  Yingyue Luna Luan, Luning Sun, Yeun Joon Kim, Jindong Wang, Xing Xie · 11 août 2026

  Prior research often finds that AI creativity is limited: single systems rarely outperform humans, and human-AI collaboration does not exceed human output. We argue these conclusions underestimate AI's potential because most studies do not allow iterative, multi-agent exchanges that mirror the socia…

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

  Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin · 10 août 2026

  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.g., story generation) as well as those that require it implicitly, e.g., reinforcement learning (…

• Recipes for Creativity: Iterative Generation and Evaluation in Large Language Models

  Rens Anderson, Tessa Verhoef, Amirhossein Zohrehvand · 10 août 2026

  Generative models are often evaluated through singular artifacts, whereas human creativity typically emerges through iterative generation, appraisal, and refinement. This pilot study examines whether iterative search improves LLM creativity by adapting FunSearch to recipe generation for the 2024 Pil…

• Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding

  Ming Wang, Yuqing Zhang, Tingna Xie, Xiangju Li, Xiaocui Yang, Daling Wang, Shi Feng, Yifei Zhang · 10 août 2026

  Creative capabilities of MLLMs matter in design, communication, education, and human--AI collaboration, yet remain difficult to evaluate because explicit targets and reward signals are scarce compared with accuracy-oriented tasks. Cross-concept understanding is a core cognitive capacity underlying r…

• Creative Integration: A Decidable Criterion of Creativity

  Yoshinori Nomura · 3 août 2026

  "Integrative" solutions are widely praised but rarely defined: we lack an operational way to tell a genuine integration -- one that makes the world cheaper to describe -- from a tidy re-description. Building on the lineage that treats creativity and intelligence as compression, we give such a criter…