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Noam Brown

Author of "Implications of Large-Scale Test-Time Compute," arguing benchmark and safety evaluations should measure performance against inference budget.

Noam Brown is the author of "Implications of Large-Scale Test-Time Compute," an X Article arguing that frontier-model capability measurement should treat inference budget as a first-class variable rather than reporting single scalar benchmark scores.¹

The article connects Test-Time Compute Evaluations to AI preparedness: if model capability increases with tokens, dollars, or wall-clock time at inference, then safety evaluations and release thresholds need to specify and vary the inference budget they test against.¹