CombEval: A Framework for Evaluating Combinatorial Counting in Large Language Models
We present CombEval, a dynamic benchmark for evaluating combinatorial counting in large language models. CombEval represents each problem as a typed Cofola specification over entities, combinatorial objects, object dependencies, and constraints, enabling controlled generation of natural-language counting problems with exact solver-verified answers. Unlike static collections, CombEval supports systematic variation of object type, entity scale, constraint count, and reasoning depth. We evaluate 11 LLMs under direct and code-augmented settings and find that models remain brittle on ordered objects, indistinguishable elements, relatively positional constraints, and nested object dependencies....
| Authors | Yuxu Zhou, Ondřej Kuželka, Yuyi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 June 2026 |
| Last updated at source | 30 September 2026 |
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