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Lancaster University
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ECAISA shifts the paradigm from certifying AI safety to ensuring rigorous auditability, addressing critical gaps in alignment research.
Evasive steganographic payloads in LLMs can retain high recoverability while eluding detection, but targeted data interventions can restore visibility to these hidden secrets.
