A theoretical framework becomes computationally mature when its admissible equations, branch structure, and observational map are organized into a numerical research program rather than remaining purely formal. In the finite-capacity latency–erasure program, previous works have already established falsification logic, parameter closure, mathematical admissibility, nonlinear branch selection, covariant completion, and observational inference geometry. The next required step is therefore computational: to determine how the admissible theory manifold should be sampled, how benchmark scenarios should be defined, how synthetic observables should be generated, and how a multi-sector forecast pipeline should be implemented. In this paper, we develop the numerical program of the latency–erasure theory. We define a computationally admissible theory domain obtained by imposing covariant, weak-field, nonlinear, and observational pre-filters together with explicit numerical-regularity and solver-pathology exclusions. Within this domain we introduce benchmark scenario families representing weak-field-dominated, cosmology-dominated, history-memory-dominated, fluctuation-dominated, and balanced multi-sector realizations. These families are organized geometrically through benchmark foliation and sector-dominance coordinates. Each benchmark family is propagated through a synthetic observable map producing mock weak-field, cosmological, clock, and stochastic outputs. These are then assembled into a multi-sector synthetic forecast architecture using structured covariance models, sensitivity matrices, synthetic likelihood landscapes, forecast information geometry, and decisiveness scores. The analysis is strengthened in four directions. First, the computational domain is treated not as a raw parameter box but as a charted numerical manifold equipped with scale-adapted coordinates, branch-aware continuation structure, and tractability filters. Second, the synthetic-forecast program is extended beyond forward observables to a mock-inference architecture with synthetic likelihoods, family posterior masses, and local sensitivity diagnostics. Third, decisive-island search is formulated as a structured optimization problem favoring robust high-score plateaus over isolated tuned peaks. Fourth, we propose a full benchmark-dossier reporting standard including branch labels, solver settings, synthetic observable panels, Jacobian spectra, information spectra, decisiveness metrics, and numerical conditioning diagnostics. Several results follow. Not all admissible theory points are equally useful numerically; some are computationally stiff, observationally redundant, or branch-ambiguous, while others occupy decisive islands that are both stable and highly informative. Benchmark-family logic is preferable to raw unstructured scanning because it preserves interpretability across sectors while still allowing systematic exploration of the admissible manifold. Synthetic forecast architecture makes it possible to compare candidate theory regions before full confrontation with real data, thereby identifying where effort should be concentrated. A decisive-island search algorithm can be formulated using admissibility filters, branch selection, information conditioning, and multi-sector decisiveness criteria. Finally, a reporting standard is proposed for future numerical work consisting of benchmark tables, observable panels, Jacobian spectra, information eigenmodes, stiffness metrics, and decisiveness maps. The main result is that the latency–erasure program is sufficiently structured to support a benchmark-based numerical research architecture in which admissible branch-selected theory points can be propagated through synthetic multi-sector forecasts, enabling decisive-island searches and data-ready model evaluation. This moves the program from theoretical readiness and observational readiness to computational readiness.
Ali Caner Yücel (Sun,) studied this question.