Imagine an AI that can not only “see” images but also interpret complex questions about them, like “What color is the object to the left of the blue ball?” This is the challenge of Visual Question Answering (VQA), a field pushing the boundaries of AI perception and reasoning. Traditional VQA models often struggle with these layered questions. Researchers have been exploring the potential of “Programmatic” VQA (PVQA), where large language models (LLMs) generate code to solve the problem step by step, like giving the computer a set of instructions. But even LLMs aren't perfect coders. Enter PyramidCoder, a new framework for building PVQA models. Think of it as a three-stage process: rephrasing the question, generating code solutions, and choosing the best answer. First, PyramidCoder takes the original question and rephrases it in multiple ways, like brainstorming different approaches to solving a puzzle. This helps ensure that the underlying meaning is captured, regardless of how the question is phrased. Then, it generates several code candidates for each rephrased question, much like a team of programmers each taking a different approach. This creates a diversity of potential solutions, increasing the chances of success. Finally, an “answer aggregator” filters through the generated code snippets and their results, evaluating them and choosing the best answer. Instead of simple majority voting, this aggregator uses the LLM's power to verify that the answer aligns with the question’s intent, reducing the risk of inaccurate responses. Tested on datasets known for their complexity, PyramidCoder outperformed existing models. Its ability to explore multiple solutions through rephrasing and diversified code generation led to more accurate and robust results, particularly in scenarios involving logical inference, comparisons, or multiple choice options. Interestingly, PyramidCoder’s strength isn't tied to a single LLM. It performed well with both specialized “code” LLMs and more general-purpose ones, opening doors to wider adoption. While the technology is still developing, PyramidCoder's innovative approach represents a significant step towards creating VQA systems capable of tackling complex visual reasoning tasks. Its hierarchical structure and use of a single frozen LLM offer promising avenues for future development and wider applications across diverse domains. This could pave the way for more sophisticated AI systems that can truly understand and respond to intricate visual information, bridging the gap between human perception and machine vision.