InteractBench: Benchmarking LLMs on Competitive Programming under Unrevealed Information
Competitive programming is increasingly being used to evaluate the algorithmic reasoning capabilities of large language models (LLMs). However, existing benchmarks primarily focus on full-information tasks where all problem inputs are provided upfront. This overlooks a critical dimension of algorithmic reasoning: the ability of generated programs to operate when key information is not revealed upfront. Interactive problems, a distinctive component of competitive programming, embody this challenge. These problems require programs to engage in multi-round interaction with an interactor (a judge program) under strict protocol constraints and limited query budgets, with new information reveal...
| Authors | Jiaze Li, Aocheng Shen, Bing Liu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
Models and datasets referencing this paper
- kmsgk/InteractBenchDataset
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