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This work introduces EAVer, an End-to-end Agentic Verifier that learns to control the complete response-level verification workflow as a unified policy, and construct 794 bidirectional same-trajectory preference pairs that keep claim grouping, search, and evidence fixed, enabling decision-focused Direct Preference Opti...
CAFAD is proposed, a plug-and-play detection framework that combines multi-domain acoustic feature fusion and temporal pyramid matching for variable-length adversarial audio detection that achieves an average detection accuracy of 99.25%, with a false positive rate of 1.00% on benign samples.
This work identifies shared rank-one reuse as the root cause of the leakage exploited by the authors' attacks, and proposes LatticeLeak, which exploits the resulting hidden lattice.
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