This position paper explores pluri-perspectivism as a core element of human creative experience and its relevance to human-robot co-creativity. We propose a layered, five-dimensional model to guide the design of co-creative behaviors and the analysis of interaction dynamics. This model is based on literature and results from an interview study we conducted with 10 visual artists and 8 arts educators, examining how pluri-perspectivism supports creative practice. The findings of this study provide insight how robots could enhance human creativity through adaptive, context-sensitive behavior, demonstrating the potential of pluri-perspectivism. This paper outlines future directions for integrating pluri-perspectivism with vision-language models (VLMs), to support context sensitivity in co-creative robots.

Large language models have emergent capabilities that come unexpectedly at scale, but we need a theoretical framework to explain why and how they emerge. We prove that language models are actually non-ergodic systems while providing a mathematical framework based on Stuart Kauffman's theory of the adjacent possible (TAP) to explain capability emergence. Our resource-constrained TAP equation demonstrates how architectural, training, and contextual constraints interact to shape model capabilities through phase transitions in semantic space. We prove through experiments with three different language models that capacities emerge through discrete transitions guided by constraint interactions and path-dependent exploration. This framework provides a theoretical basis for understanding emergence in language models and guides the development of architectures that can guide capability emergence.

The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics", using a GPT-based workflow to convert equations from literature into LaTeX and Python formats. While many digital equation representations exist, there's a lack of automated large-scale evaluation tools. LLMs are pivotal as linguistic user interfaces, providing natural language access for human-AI chat and formal languages for large-scale AI-assisted computational infrastructure. Given the infinite formal possibility spaces, Math Agents, which interact with math, could potentially shift us from "big data" to "big math". Math, unlike the more flexible natural language, has properties subject to proof, enabling its use beyond traditional applications like high-validation math-certified icons for AI alignment aims. This project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology by applying multiscalar physics mathematics to disease models and genomic data. Generative AI with episodic memory could help analyse causal relations in longitudinal health records, using SIR Precision Health models. Genomic data is suggested for addressing the unsolved Alzheimer's disease problem.

The Game Developer's Conference, the largest event in the games industry, has hosted over 50 talks in the last decade about procedural generation, from small-scale independent speakers to large AAA companies, covering disciplines from programming to art to writing. Correspondingly, procedural generation has been an increasingly hot topic among game AI researchers in the last two decades. The Procedural Generation Workshop at FDG, now in its twelfth year, is one of the longest-running workshops in the field of game AI, and dedicated paper tracks at conferences are a regular occurrence. Despite the huge importance of content generation, and the wealth of time invested into developing practical techniques, the analysis of procedural generators is a relatively underdeveloped area of study. A few notable techniques have emerged over the last two decades of research [7, 8], as well as studies of efficacy [4, 9], but many of the techniques used by game researchers have changed little in that time. As a result, a lot of procedural generation work is done by'feel', with postmortems shared at events such as the Roguelike Celebration

The latent space modeled by generative adversarial networks (GANs) represents a large possibility space. By interpolating categories generated by GANs, it is possible to create novel hybrid images. We present "Meet the Ganimals," a casual creator built on interpolations of BigGAN that can generate novel, hybrid animals called ganimals by efficiently searching this possibility space. Like traditional casual creators, the system supports a simple creative flow that encourages rapid exploration of the possibility space. Users can discover new ganimals, create their own, and share their reactions to aesthetic, emotional, and morphological characteristics of the ganimals. As users provide input to the system, the system adapts and changes the distribution of categories upon which ganimals are generated. As one of the first GAN-based casual creators, Meet the Ganimals is an example how casual creators can leverage human curation and citizen science to discover novel artifacts within a large possibility space.

Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content. In this paper, we argue that TTRPG design can usefully be viewed as procedural content generator design. We present several case studies linking key concepts from PCG research -- including possibility spaces, expressive range analysis, and generative pipelines -- to key concepts in TTRPG design. We then discuss the implications of these relationships and suggest directions for future work uniting research in TTRPGs and PCG.

The beat generation poet Philip Lamantia believed that in order to create authentic writing one had to first reach a trance-like state in between sleep and wakefulness - a place of the primal sources of creativity that, according to him, could be attained with the help of a little peyote. A thousand years before that the poet Homer was invoking the muses of Grecian myths in reach a similar state of inspiration, suggesting that as far back as the writing of The Odyssey, storytelling was viewed as a partnership with something other than human. What about stories developed in partnership with artificial intelligence? This can feel at odds with the romantic view of storytelling, where the author inhales creative inspiration and exhales exacting prose. But perhaps it presents another way of understanding the muse - an electric muse working in partnership with the artist.

We'll start with a fun one that tries to predict your strokes as you write Neural networks are an extremely successful approach to machine learning, but it's tricky to understand why they behave the way they do. This has sparked a lot of interest and effort around trying to understand and visualize them, which we think is so far just scratching the surface of what is possible. In this article we will try to push forward in this direction by taking a generative model of handwriting2 and visualizing it in a number of ways. In the end we don't have some ultimate answer or visualization, but we do have some interesting ideas to share. Ultimately we hope they make it easier to divine some meaning from the internals of these model.

This piece showcased the immense power of exponential technology versus linear technology and became a pivotal concept for anyone trying to anticipate what the future held. The essay predicted advances in business and technology with eerie precision, including how exponential growth would ripple through any technology that became an information technology, such as computing, biotechnology, or energy. The past 15 years have shown that while some of Kurzweil's specific predictions may or may not pan out exactly as predicted, the underlying idea of the law of accelerating returns grows more relevant with each passing week. But as we start to look at the next fifteen years, I believe there is another concept just as significant as the law of accelerating returns that we need to understand. The strangest, most interesting and magical-seeming creations of the future will occur at the intersection of multiple exponential trend lines.

The Prom is a game where the player manages the social life of a group of high school students and creates the situations from which dramatic, thought provoking or at least funny stories can unfold. The setting of The Prom involves a group of alternative high school kids (e.g. Emos, Goths, Geeks, etc.) and their dramatic lives as they prepare for the upcoming school prom. Through creating friendships, making people become enemies, controlling who gets to be in the "in" crowd and much more, the player can shape the social world of the characters. Each character has a distinct personality represented by interests (e.g. what bands they like), needs (e.g. a character may need to demonstrate a certain degree of dominance over others), traits (e.g. being a particularly jealous person), social networks (e.g. to what degree a characters like, are attracted to or respect one another) and social status (e.g. who is dating who).The social artificial intelligence system Comme il Faut ( CiF ) drives this gameplay experience by simulating per character needs and traits, social statuses, social networks, social history and most importantly to gameplay, the outcomes and effects of social games. CiF is a playable computational model of social interactions designed specifically to allow autonomous characters to play social games. By giving player controls to navigate a social, rather than physical, space, The Prom is being created to demonstrate how CiF and social games can create a practically limitless numbers of possibly compelling stories and gameplay.