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The End of the Train-Test Split

Exploring the shift from static dataset evaluation to dynamic, agentic benchmarking in the era of capable AI models.

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The Problem: Data Contamination

LLMs are trained on trillions of tokens, including GitHub repositories, StackOverflow, and academic papers.

  • The Leak: If you test a model on a coding problem from 2022, chances are the model saw the solution in its training data.
  • The Illusion: The model isn't reasoning to solve the problem; it's remembering the solution.

This leads to inflated benchmark scores that don't reflect real-world performance.

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