VulcanBench to adopt terse CursorBench prompts

Evaluating coding agents using overly descriptive prompts creates an artificial ceiling, making real-world performance seem better than it is. Moving toward terse prompts forces benchmarks to measure how agents handle actual, messy developer intent.

• –Developers rarely write perfect, multi-paragraph prompts, making terse prompt benchmarks much more representative of real-world tool usage.
• –Terse prompts require coding agents to perform much more autonomous context engineering and codebase exploration to understand the task.
• –VulcanBench's transition to this model offers a transparent, reproducible alternative to proprietary evaluation suites like CursorBench.
• –As synthetic benchmarks become saturated, the battleground for AI coding evals is shifting toward recreating the ambiguity of day-to-day software development.