Trading Inference-Time Compute for Adversarial Robustness
Wojciech Zaremba; Evgenia Nitishinskaya; Boaz Barak; Stephanie Lin; Sam Toyer; Yaodong Yu; Rachel Dias; Eric Wallace · 2025 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
We conduct experiments on the impact of increasing inference-time compute in reasoning models (specifically OpenAI o1-preview and o1-mini) on their robustness to adversarial attacks. We find that across a variety of attacks, increased inference-time compute leads to improved robustness. In many cases (with important exceptions), the fraction of model samples where the attack succeeds tends to zero as the amount of test-time compute grows. We perform no adversarial training for the tasks we study, and we increase inference-time compute by simply allowing the models to spend more compute on reas
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Metadata source: arXiv