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This paper empirically investigates shortcut learning in legal judgment prediction using UK Employment Tribunal claims, finding that models exploit outcome-revealing cues in post-hoc judicial texts, but performance remains robust after masking such leakage features.
MosaicLeaks introduces a new benchmark for measuring privacy leakage in deep-research AI agents, showing that agents often leak private information through external queries and proposing a training method (PA-DR) to reduce leakage while improving task performance.
Introduces MosaicLeaks, a benchmark of 1,001 multi-hop deep research tasks that chain private enterprise documents with public web queries to evaluate privacy leakage. Finds that models leak sensitive information at multiple levels, and proposes PA-DR, a reinforcement learning framework that reduces leakage while improving task accuracy.