Optimize Coding LLM for Reasoning or Tools?
AI Summary
The video presents Live Code Benchmark Pro, a new competitive AI coding benchmark designed by a team including international Olympiad and informatics medalists. Unlike older benchmarks, it evaluates how AI models reason during code generation, not just whether they solve a problem. The benchmark categorizes tasks into three cognitive skills: knowledge-heavy, logic-heavy (chain of reasoning), and observation-heavy (requiring creativity and insight). Results show that current AI code models excel at knowledge and logic tasks but fail completely on observation-heavy problems involving creative insights. AI struggles with the conceptual phase of problem-solving but is strong at implementing correct code once the idea is given. The benchmark reveals a significant reasoning gap and challenges claims that AI has surpassed elite human coders. Additionally, it highlights the potential of tool-assisted AI coding models and poses future directions: building inherently better reasoners or smaller models with advanced tool use. Overall, the benchmark provides deep insights into AI coding abilities, failures, and opportunities for future improvements. The live benchmark and code repository are publicly available for further exploration.