Static benchmarks are like a still photograph of a sprinting athlete—they capture a moment but not the whole story. Similarly, traditional benchmarks struggle to truly assess the rapidly evolving capabilities of large language models (LLMs). They offer a fixed snapshot of performance but can't adapt to the models' increasing complexity or account for potential data contamination. Imagine a test designed for a high school student being given to a college graduate. It wouldn't accurately reflect the graduate's knowledge, would it? That's the challenge with static LLM benchmarks. Enter DARG, or Dynamic Evaluation of LLMs via Adaptive Reasoning Graph Evolvement. This innovative framework tackles the limitations of static tests by dynamically extending them with controlled complexity and diversity. Instead of using fixed test questions, DARG creates a “reasoning graph” representing the underlying logic of a problem. It then perturbs this graph, generating new, more complex versions of the original problem while preserving the linguistic style of the original dataset. Think of it like starting with a basic math problem and adding more variables or steps, making it progressively harder while still sounding like a real-world scenario. To ensure accuracy, DARG uses a code-augmented LLM to verify the correctness of the new questions and answers. This external check helps reduce the LLM's tendency to hallucinate or make mistakes, providing a more reliable evaluation. Across various reasoning tasks—math, social scenarios, spatial navigation, and symbolic manipulation—DARG revealed a consistent trend: LLMs struggle as complexity increases. The more steps or variables involved, the more likely the models were to make errors. Surprisingly, larger models, which typically excel on standard benchmarks, often showed significant performance drops under DARG's dynamic tests. Furthermore, DARG revealed an increase in biases within some LLMs as complexity rose, particularly in social reasoning tasks. This finding highlights the importance of dynamic evaluation in uncovering hidden biases that might not surface in simpler tests. The implications of DARG extend beyond evaluation. The data generated by DARG can be used to fine-tune existing models, making them more robust and adaptable to increasing complexities. In the fast-paced world of AI, static benchmarks are becoming obsolete. DARG offers a path toward more dynamic, reliable, and insightful LLM evaluation, helping us build truly capable and unbiased AI systems.