CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915915244765184 |
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| author | Ismayilzada, Mete Cuomao, Renqing Yurshevich, Daniil Sotnikova, Anna van der Plas, Lonneke Bosselut, Antoine |
| author_facet | Ismayilzada, Mete Cuomao, Renqing Yurshevich, Daniil Sotnikova, Anna van der Plas, Lonneke Bosselut, Antoine |
| contents | Creative problem-solving requires combining multiple cognitive abilities, including logical reasoning, lateral thinking, analogy-making, and commonsense knowledge, to discover insights that connect seemingly unrelated pieces of information. However, most existing benchmarks for large language models (LLMs) evaluate only specific components of this process. Moreover, many creativity-oriented benchmarks rely on artificially constructed brainteasers or contrived scenarios that do not reflect how creative problem-solving occurs in real-world settings. To address this gap, we introduce CresOWLve, a benchmark for evaluating creative problem-solving using puzzles grounded in real-world knowledge. Problems in CresOWLve require employing multiple creative thinking strategies, retrieving facts from diverse domains, and creatively combining them to arrive at a solution. Evaluating several frontier non-thinking and thinking LLMs, we show that CresOWLve remains highly challenging. Our analysis reveals a consistent performance gap: models perform substantially better on factual questions than on creative ones (up to a -17% drop). While models can often retrieve the relevant knowledge, they struggle to form the non-obvious creative connections required to integrate this information and arrive at the correct answer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03374 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge Ismayilzada, Mete Cuomao, Renqing Yurshevich, Daniil Sotnikova, Anna van der Plas, Lonneke Bosselut, Antoine Computation and Language Artificial Intelligence Creative problem-solving requires combining multiple cognitive abilities, including logical reasoning, lateral thinking, analogy-making, and commonsense knowledge, to discover insights that connect seemingly unrelated pieces of information. However, most existing benchmarks for large language models (LLMs) evaluate only specific components of this process. Moreover, many creativity-oriented benchmarks rely on artificially constructed brainteasers or contrived scenarios that do not reflect how creative problem-solving occurs in real-world settings. To address this gap, we introduce CresOWLve, a benchmark for evaluating creative problem-solving using puzzles grounded in real-world knowledge. Problems in CresOWLve require employing multiple creative thinking strategies, retrieving facts from diverse domains, and creatively combining them to arrive at a solution. Evaluating several frontier non-thinking and thinking LLMs, we show that CresOWLve remains highly challenging. Our analysis reveals a consistent performance gap: models perform substantially better on factual questions than on creative ones (up to a -17% drop). While models can often retrieve the relevant knowledge, they struggle to form the non-obvious creative connections required to integrate this information and arrive at the correct answer. |
| title | CresOWLve: Benchmarking Creative Problem-Solving Over Real-World Knowledge |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.03374 |