META–CAUSAL: The Geometry of Predictive Memory and the Limits of Reconstructibility

META–CAUSAL: The Geometry of Predictive Memory and the Limits of Reconstructibility investigates a fundamental question: how much internal memory is required to reproduce an observable process, and how much of its hidden structure can be recovered from measurements? The monograph connects predictive memory, spectral invariants, and operational reconstruction across classical stochastic and quantum models. Its central perspective is that a simple observed signal need not admit a simple internal realization. Memory requirements depend on positivity, probability normalization, accessible measurements, and the distinction between exact reproduction and approximation. A principal result turns the approximation of a periodic spike into an exact fractional matching problem on a divisor hypergraph. For a spike of amplitude b, with 0 < b ≤ 1, repeating every n steps, the optimal uniform error of an m-state homogeneous Markov model, where 1 ≤ m < n, is Eₘ = b/(1 + νₘ). Here νₘ is the largest fractional matching mass attainable within the available cycle budget. This correspondence produces explicit stochastic constructions and matching lower bounds, revealing a staircase of memory thresholds. For period 210, the exact values include E₁₀₀ = 2b/7 and E₁₀₇ = b/4; period 1050 exhibits an additional plateau at 3b/8. A universal rational polytope organizes the possible error levels through determinant formulas, while a structural analysis identifies the final three thresholds for an arbitrary number of distinct prime divisors without enumerating the polytope’s vertices. The broader investigation examines what observations preserve, what they conceal, and what remains certifiable under noise. Spectral witnesses, positive realizations, finite-sample topological certificates, and geometric models involving S³, Hopf structures, and Berger metrics address complementary aspects of this problem. In open quantum systems, the distinction between a damping gap and a purity gap separates dynamical relaxation from the stability of stationary-state topology. The resulting framework combines exact mathematical statements, rational computational certificates, and explicitly conditional physical models. Its contribution is a concrete account of how algebraic, combinatorial, and geometric structure governs both the cost of prediction and the limits of reconstruction.