A benchmark was developed to assess the ability of 23 large language models (LLMs) to solve nonograms, which are grid-based logic puzzles. The evaluation revealed that performance significantly declines as the puzzle size increases from 5×5 to 15×15. Some models resort to generating code for brute-force solutions, while others demonstrate a more human-like reasoning approach by solving puzzles step-by-step. Notably, GPT-5.2 leads the performance leaderboard, and the entire benchmark is open source, allowing for future testing as new models are released. Understanding how LLMs approach problem-solving in logic puzzles can provide insights into their reasoning capabilities and potential applications.
The exploration of large language models (LLMs) and their ability to solve nonograms—a type of grid-based logic puzzle—sheds light on the evolving capabilities of artificial intelligence in handling complex reasoning tasks. Nonograms require a combination of logic and pattern recognition, making them an intriguing test for evaluating the reasoning abilities of LLMs. The benchmark study involving 23 different models reveals that while some models attempt to brute-force solutions through code generation, others exhibit a more human-like approach by reasoning through the puzzles step-by-step. This distinction is crucial as it highlights the varying methodologies LLMs employ to tackle logical challenges.
One of the most significant findings is the sharp decline in performance as the size of the puzzles increases. This suggests that while LLMs may handle simpler puzzles with relative ease, their capacity to manage more complex, larger-scale problems is still limited. This limitation is particularly pertinent as it underscores the challenges faced in scaling AI reasoning capabilities. The ability to solve larger puzzles could be indicative of a model’s potential to handle more intricate real-world problems, making this an essential area for further research and development.
GPT-5.2 emerges as a standout performer, dominating the leaderboard in this benchmark. Its success may point to advancements in model architecture or training techniques that enable it to better simulate human-like reasoning. However, the question remains whether these models are truly reasoning or merely employing sophisticated problem-solving strategies. This distinction matters because it affects how we perceive the capabilities of AI and its potential applications. Understanding whether LLMs can genuinely reason or are simply executing complex algorithms is critical for assessing their future roles in decision-making processes and problem-solving tasks.
The open-source nature of this benchmark allows for continuous testing and improvement as new models are developed. This transparency is valuable for the AI community, fostering collaboration and innovation. By providing a platform for ongoing evaluation, researchers and developers can better understand the strengths and weaknesses of different models, driving progress in AI reasoning abilities. The cost of curiosity, as noted, was around $250, covering approximately 17,000,000 tokens, yet the insights gained are invaluable for shaping the future of AI. As we continue to refine these models, the potential for AI to tackle increasingly complex tasks becomes more tangible, promising advancements across various fields.
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