While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to tackle these difficulties in the domain of chess puzzles. We start by benchmarking Generative AI architectures, and then introduce an RL framework with novel rewards based on chess engine search statistics to overcome some of those shortcomings. The rewards are designed to enhance a puzzle's uniqueness, counter-intuitiveness, diversity, and realism. Our RL approach dramatically increases counter-intuitive puzzle generation by 10x, from 0.22% (supervised) to 2.5%, surpassing existing dataset rates (2.1%) and the best Lichess-trained model (0.4%).

Multimodal large language models (MLLMs) face an inherent trade-off between faithfulness and creativity, as different tasks require varying degrees of associative reasoning. However, existing methods lack the flexibility to modulate this reasoning strength, limiting MLLMs' adaptability across factual and creative scenarios. To bridge this gap, we propose equipping MLLMs with mechanisms that enable flexible control over associative reasoning. We begin by investigating the internal mechanisms underlying associative behavior in MLLMs and find that: (1) middle layers play a pivotal role in shaping model's associative tendencies, (2) modifying representations in these layers effectively regulates associative reasoning strength, and (3) hallucinations can be exploited to derive steering vectors that guide this modulation. Building on these findings, we introduce Flexible Association Control (FlexAC), a lightweight and training-free framework for modulating associative behavior in MLLMs.

Recent text-to-image models have revolutionized image generation, but they still struggle with maintaining concept consistency across generated images. While existing works focus on character consistency, they often overlook the crucial role of scenes in storytelling, which restricts their creativity in practice. This paper introduces scene-oriented story generation, addressing two key challenges: (i) scene planning, where current methods fail to ensure scene-level narrative coherence by relying solely on text descriptions, and (ii) scene consistency, which remains largely unexplored in terms of maintaining scene consistency across multiple stories. We propose SceneDecorator, a training-free framework that employs VLM-Guided Scene Planning to ensure narrative coherence across different scenes in a ``global-to-local'' manner, and Long-Term Scene-Sharing Attention to maintain long-term scene consistency and subject diversity across generated stories. Extensive experiments demonstrate the superior performance of SceneDecorator, highlighting its potential to unleash creativity in the fields of arts, films, and games.

Recent large language models (LLMs) with long-chain-of-thought reasoning--such as DeepSeek-R1--have achieved impressive results on Olympiad-level mathematics benchmarks. However, they often rely on a narrow set of strategies and struggle with problems that require a novel way of thinking. To systematically investigate these limitations, we introduce OMEGA--Out-of-distribution Math Problems Evaluation with 3 Generalization Axes--a controlled yet diverse benchmark designed to evaluate three axes of out-of-distribution generalization, inspired by Boden's typology of creativity: (1) Exploratory--applying known problem-solving skills to more complex instances within the same problem domain; (2) Compositional--combining distinct reasoning skills, previously learned in isolation, to solve novel problems that require integrating these skills in new and coherent ways; and (3) Transformative--adopting novel, often unconventional strategies by moving beyond familiar approaches to solve problems more effectively. OMEGA consists of programmatically generated training-test pairs derived from templated problem generators across geometry, number theory, algebra, combinatorics, logic, and puzzles, with solutions verified using symbolic, numerical, or graphical methods. We evaluate frontier (or top-tier) LLMs and observe sharp performance degradation as problem complexity increases. Moreover, we fine-tune the Qwen-series models across all generalization settings and observe notable improvements in exploratory generalization, while compositional generalization remains limited, and transformative reasoning shows little to no improvement. By isolating and quantifying these fine-grained failures, OMEGA lays the groundwork for advancing LLMs toward genuine mathematical creativity beyond mechanical proficiency.

Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objectives in open-ended text generation: fostering diversity and creativity while preserving logical coherence. Existing truncated sampling techniques, including temperature scaling, top- (nucleus) sampling, and min-sampling, aim to manage this trade-off.

We sat down with Douglas Guillbault to discuss his paper, " Comparing Colorseeing, Colorblind, Painters, and Large Language Models in Their Processing of Color Metaphors ". The results have interesting implications for how we model human cognition, and in turn, how the concept of synaesthesia could be integrated to develop more intelligent AI models. A color metaphor is the use of color to describe something in a way that is not immediately literal. For example, to say "green with envy" would be a color metaphor, because envy doesn't have an immediate visual structure to it - we're evoking a broader, more flexible notion of what green conveys, beyond just its visible properties. What makes metaphors very interesting is that they often use past experience or cultural associations in new ways to talk about something beyond our current perception - either something imagined or in the future, which are many steps of abstraction away from the present. Metaphors provide an alternative pathway to get there.

Multimodal large language models (MLLMs) face an inherent trade-off between faithfulness and creativity, as different tasks require varying degrees of associative reasoning. However, existing methods lack the flexibility to modulate this reasoning strength, limiting MLLMs' adaptability across factual and creative scenarios. To bridge this gap, we propose equipping MLLMs with mechanisms that enable flexible control over associative reasoning. We begin by investigating the internal mechanisms underlying associative behavior in MLLMs and find that: (1) middle layers play a pivotal role in shaping model's associative tendencies, (2) modifying representations in these layers effectively regulates associative reasoning strength, and (3) hallucinations can be exploited to derive steering vectors that guide this modulation. Building on these findings, we introduce Flexible Association Control (FlexAC), a lightweight and training-free framework for modulating associative behavior in MLLMs.

Our cousins'Homo juluensis' knew how to adapt in the face of an ice age. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. One of the 146,000-year-old stone cores used to make butcher's tools, found in Lingjing, China. Breakthroughs, discoveries, and DIY tips sent six days a week. A remarkable collection of ancient stone tools proves that human creativity can thrive in challenging times.

Peter Molyneux OBE is reflecting upon the future of the UK games industry in his office - and how he could soon be leaving it. The 66-year-old, who over the years has helped create iconic series such as Fable, Black & White and Theme Park, tells me Masters of Albion - his latest project as creative director of 22cans - will also be his final one. He sees it as a return to his roots - a reinvention of the god game - a genre he introduced with Populous in 1989, one where players play as a deity on high, controlling a population's inhabitants as they please. In this new iteration, players are able to build and manage settlements by day, before defending them from attacks at night, with the ability to take control of individual characters at any point. For Molyneux, once voted one of the top game creators of all time, the key idea is freedom - creating systems that respond to player curiosity rather than directing them down a fixed path.

Electronic music artist and tech creator Look Mum No Computer has been chosen to represent the UK at this year's Eurovision Song Contest in Vienna, the BBC has announced. Look Mum No Computer is a solo artist, songwriter and YouTuber, who is also described as an inventor of unique musical machines. The singer first arrived on the music scene back in 2014 as Sam Battle, frontman of indie rock band Zibra. The group performed at Glastonbury in 2015 for BBC Introducing. Since then, he has been performing and recording under his solo name.