Selective ML-Guided Beam Search for Differential-Trail Discovery in GIFT-64 and PRESENT-64

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Beam search is practical for differential-trail exploration, but its cost grows quickly with the number of reachable intermediate differences. We test whether a lightweight tail-cost estimate can reduce deterministic bounded beam-search work without taking control of the search. At each horizon, a regressor predicts a residual tail cost from an active-nibble heuristic using the current difference and target endpoint. The learned signal is applied after classical selection. With four or two round