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cs.LG2026
Predicting LLM Safety Before Release by Simulating Deployment
Marcus Williams, Hannah Sheahan, Cameron Raymond +8
Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model. Yet most evaluations provide limited evidence about how often undesired model beha…
cs.LG2026★ 1 cited
Fast Explanations via Policy Gradient-Optimized Explainer
Deng Pan, Nuno Moniz, Nitesh Chawla
The challenge of delivering efficient explanations is a critical barrier that prevents the adoption of model explanations in real-world applications. Existing approaches often depe…