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This work proposes ForeSight, a first-token output-risk forecasting framework that distills weak and redundant early safety signals into compact, layer-aware risk representations and achieves superior and efficient early-risk forecasting while relying solely on first-token hidden states.
The Consistency Radius is introduced, a metric that quantifies the maximum distribution shift under which an audit conclusion based on a third-party dataset remains consistent, and a convex relaxation-based optimization method to estimate the radius using only model responses over the audit dataset is proposed.
RecVerse is presented, a GUI-grounded simulation agent that perceives pages through screenshots and produces faithful multi-turn trajectories and significantly outperforms existing baselines in both behavioral fidelity and intent consistency.
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