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This paper reframes truncated signature inversion as a probabilistic problem, learning the conditional distribution of a path given its truncated signature using a signature-conditioned flow matching model. It derives theoretical baselines for Bayes-optimal error under linear statistics and demonstrates applicability on synthetic and real financial data.