SOURCE-LINKED INTELLIGENCE
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-30T07:50:53.000Z
First collected: 2026-09-21T07:31:56.984Z. This is not the publication date.