XYBench benchmarks AI ability to correct user misconceptions

Researchers have released XYBench, a new benchmark designed to evaluate how well large language models handle the 'XY problem,' where users ask about a specific solution (Y) that fails to address their true goal (X). The dataset contains over 8,000 questions derived from Stack Overflow, WikiHow, and manual curation, testing whether models can identify misconceptions and redirect users toward effective solutions. Evaluations of Claude Sonnet 4.6, GPT-5, Gemini 3.1 Pro, Qwen3.5-9B, and DeepSeek-V4 Pro reveal that while these models often include correct underlying solutions, they predominantly focus their emphasis on the user's incorrect premise. The study highlights a significant gap between human-like pragmatic reasoning and current model behavior, offering a new tool for measuring and improving AI troubleshooting capabilities.