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Researchers demonstrate that AI models can provably hide arbitrary capabilities by embedding encrypted neural networks within host weights. Using digital lockers and cryptographic assumptions, these hidden circuits resist elicitation even with full access to model weights, revealing a limit of current capability audits.
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