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A training-free framework that reformulates failure recovery as test-time scaling over causal histories, which decomposes recovery into three coupled decisions: when to revise the causal history, where to recover a reliable history prefix, and which history configuration best supports subsequent execution.
Injecting calibrated noise from RRAM's inherent randomness allows for high-utility, privacy-preserving training with minimal accuracy loss and substantial energy savings.
