AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Model-attribution classifiers can often identify which language model produced a text, making model-specific writing patterns a signal of provenance. Accurate attribution on unmodified text, however, does not show whether the prediction still identifies the original source after deliberate rewriting. We formulate this...
Together, these results give a substitution map for privacy accounting: when Poisson-based computations remain sound for structured participation, where they fail, and what sound alternatives cost in deployment.
The results indicate that diffusion transformers carry more concept-level information than current attribution methods recover, and that much of it is lost on the way to the mask rather than absent from the model.
The exact DP constant is pin down for the two that carry the practical weight, counterfactual memorization and adaptive extraction, and it is shown that they do not control each other.
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