InnoGym: Benchmarking the Innovation Potential of AI Agents
This addresses the need for benchmarks that assess both creativity and effectiveness in AI agents, which is incremental as it builds on existing evaluation frameworks.
The paper tackles the problem that existing AI benchmarks focus on correctness but overlook solution diversity, by introducing InnoGym, the first benchmark to evaluate AI agents' innovation potential using performance gain and novelty metrics on 18 real-world tasks, with experiments showing agents often lack robustness despite some novelty.
LLMs and Agents have achieved impressive progress in code generation, mathematical reasoning, and scientific discovery. However, existing benchmarks primarily measure correctness, overlooking the diversity of methods behind solutions. True innovation depends not only on producing correct answers but also on the originality of the approach. We present InnoGym, the first benchmark and framework designed to systematically evaluate the innovation potential of AI agents. InnoGym introduces two complementary metrics: performance gain, which measures improvement over the best-known solutions, and novelty, which captures methodological differences from prior approaches. The benchmark includes 18 carefully curated tasks from real-world engineering and scientific domains, each standardized through resource filtering, evaluator validation, and solution collection. In addition, we provide iGym, a unified execution environment for reproducible and long-horizon evaluations. Extensive experiments show that while some agents produce novel approaches, their lack of robustness limits performance gains. These results highlight a key gap between creativity and effectiveness, underscoring the need for benchmarks that evaluate both.