Investigating Human--AI Discrepancies via Multiple-Solution Problems
Frontier artificial intelligence (AI) models are benchmarked on whether they reach a correct answer. Yet many problems admit several correct answers and repeated attempts, by different people or by the same model resampled, trace out a distribution over them. In this work, we ask whether human and model reasoning lead to different distributions over valid solutions. Our testbed comprises 270 reasoning puzzles across five puzzle families. These multiple-solution puzzles each have 3 to 8 valid solutions and are simple enough that humans and models can solve them reliably. The resulting distributions differ markedly: models differ from one another, yet resemble each other far more than they ...
| Authors | Zihao Wang, Francesco Insulla, Andrea Montanari |
|---|---|
| Primary category | cs.CY |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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