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State costs in Schr枚dinger bridges on graphs can be analytically incorporated via a Feynman-Kac tilt, enabling exact, non-learned solutions with linear memory growth even for massive networks.
The exact form of discarded information in concentration inequalities reveals hidden structures in martingale trajectories that can optimize probabilistic testing strategies.
