Exact Compilation and Resource Laws for Hidden Integer Feedback
Ganxuan Yang, Siqi Wen, Shuyan Yu, Jidong Yuan
Hidden rounding carries dependencies through integer memory. We study finite-memory feedback with predictable inputs that differ by one on at most $C$ releases and with every output public. A shared preparation constrains future branches until a sequence of common-input releases synchronizes the conditional registers. Preserving those constraints yields exact finite-law compilation, polynomial in support width for fixed memory and participation budgets, with constructive discrete-Gaussian transfer and calibration. A commitment hierarchy quantifies the loss from releasing dependencies early and becomes exact at depth $mC$ for memory $m$. Strict examples include three levels with one participation and a discrete-Gaussian calibration gap exceeding 0.23. Charging actual preparation time gives a privacy resource law with a square-root remainder and a leading coefficient computable in polynomial bit cost at fixed parameters. Lean refinements cover the first-order privacy analysis and binary value algorithm; separate lemmas check finite-memory algebra and paths. Certificates establish multi-memory calibration and resource effects; workload comparisons include a finite-memory prefix-error lower bound.
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