Competitive coding is a tough nut to crack, even for the most advanced AI. Large language models (LLMs) excel at many coding tasks, but often stumble when faced with the complex logic and constraints of competition-level problems. However, researchers have found a clever way to boost their performance using a technique called tree search. Imagine the AI exploring a branching tree of possible code solutions, intelligently navigating towards the optimal result. This is the essence of the Seed-CTS method, which cleverly integrates tree search with Chain-of-Thought (CoT) prompting. CoT prompting essentially encourages the AI to "think step-by-step," first outlining a solution plan in natural language before generating the actual code. The results are impressive. This approach significantly boosts the performance of open-source LLMs like Qwen2.5-Coder-32B-Instruct on challenging datasets like LiveCodeBench, even rivaling the accuracy of much larger, proprietary models. On the difficult LiveCodeBench-Hard dataset, Seed-CTS with CoT prompting achieves a pass rate of 0.351, close to the performance of top-tier models. This method is model-agnostic, meaning it can improve various LLMs. Moreover, the high-quality code solutions produced by Seed-CTS can be used to create more effective training data for future AI models. This opens up exciting possibilities for scaling up the creation of competition-level code generation and pushing the boundaries of what AI can achieve in coding competitions. While there's still work to be done, integrating tree search and CoT prompting represents a significant leap forward in AI's ability to tackle complex coding challenges.