A Luenberger Observer for P-Time Event Graphs

What the math gives to ML

The paper provides a constraint-aware Luenberger observer for P-Time Event Graphs, where latent firing times are reconstructed from partial observations while enforcing both causal lower bounds and token-lifetime upper bounds. Its transferable asset is the construction of a greatest feasible sub-approximation: estimates are propagated through a recurrent max-plus model and corrected whenever newly observed outputs make earlier latent states inconsistent. This can be transferred to neural state-space or world-model inference as a hard or differentiable projection layer that prevents latent trajectories from violating known temporal-window constraints. The falsifiable prediction is that constraint projection eliminates physically impossible rollouts and that estimation error contracts after informative observations.