I Benchmarked 4 Frontier LLMs on Catching ML's "Silent Killers" — DeepSeek-R1 Missed the Most Basic Bug
This is a submission for the Kaggle Benchmarking Challenge Most public AI leaderboards test if a model can write code or pass a syntax check. But in real-world Machine Learning, the most dangerous code isn't syntactically broken—it's methodologically flawed. It passes unit tests, shows a green dashboard, and then dies silently in production. For the Kaggle Benchmarking Challenge, I built "The…
For the Kaggle Benchmarking Challenge, the author constructed a benchmark called "The Silent Killer" to test if large language models (LLMs) could identify methodological flaws in machine learning pipelines. This benchmark focused on four specific types of "silent killers": data leakage, using the wrong metric, and target leakage.
The author evaluated four frontier LLMs against this benchmark: Gemini 3.7 Flash, Claude Sonnet 4.5, Grok 4.20 Reasoning, and DeepSeek-R1. While Gemini 3.7 Flash, Claude Sonnet 4.5, and Grok 4.20 Reasoning all successfully detected the flaws, DeepSeek-R1 failed to identify the most basic bug, missing it entirely. This discrepancy highlights a critical limitation in DeepSeek-R1's ability to comprehensively audit ML pipelines.
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