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This work recast black-box black-box LLM steganography as a sequential communication problem with causal, noiseless feedback: every generated token is observed by both parties and can guide subsequent embedding.
It is proved that the authentication capacity is positive exactly when the channel is not distribution-overwritable under stochastic encoding, and not I-overwritable under deterministic encoding.
A forecast-free reinforcement learning (RL) framework for DERA allocation that learns optimal policies directly from operational data, which preserves the interpretability and constraint satisfaction of DER model while adapting to stochastic demand variations through data-driven updates.
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