Synthetic Benchmark Generation

Synthetic benchmark generation creates evaluation tasks and datasets when real examples are scarce, costly, private, contaminated, or too fixed to expose specific weaknesses. Approaches include procedural generators and simulators, LLM- or agent-driven task synthesis, and solver-backed construction with executable checks for answers, feasibility, and difficulty. These methods support fresh, adjustable tests across areas such as reasoning, coding, robotics, retrieval, and scientific workflows, often with ground truth known by construction. A central challenge is balancing control and verifiability with realistic, diverse tasks that transfer to real settings, while avoiding artifacts or shared generation and evaluation rules that inflate scores.