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Madhava Gaikwad

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#artificial intelligence Preprint Sep 2026

Information Design Against Gaming and Learning Adversaries

The Pareto frontier between the two defense objectives is characterized, and both rates are confirmed on seven binary-classification tasks spanning tabular, image, and language-model-feature inputs: label-plus-counterfactual access extracts the boundary with up to $200\times$ fewer queries than a published label-only b...

Madhava Gaikwad · 0 citations
#artificial intelligence Preprint Sep 2026

A Translational Note on AI Safety Evaluation

The AI-safety version of the threat-model coverage gap is called the AI-safety version the threat-model coverage gap, and it is found that it persists in a current open-weight model, where harms surface in non-English prompts that English benchmarks miss.

Madhava Gaikwad · 0 citations

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