Informational Requirements for Pre-Categorical Relational Models: Identifiability, Measure, Dynamic Closure, and Coarse-Graining

Technical Research Report MFRP-TR-2026-01, version 1.1, published within the Modal Field Research Programme. This technical research report studies relational models that are asked to support cardinal comparison, refinement, transition, and coarse-graining even when their primitive data contain only a relation. It investigates which additional informational structures are required for those operations. Order-only observations are invariant under broad monotone recalibrations and therefore do not identify latent marginal scales or densities. An additional mark q can supply relative cardinal information only when its marked law and observable coupling are not determined by the relation. Additive mass μ is required to distinguish conservative refinement from representational cloning, while locality and symmetry alone leave the transition kernel K underdetermined. For a declared exponential observable, total mass and the effective score Q_lambda provide an exact static block summary. Under centered contraction, however, the next score depends on Q evaluated at a shifted parameter. An exact four-point counterexample shows that equal mean and equal Q_lambda do not determine the next score on the unrestricted finite class. Thus exact static sufficiency does not imply dynamic autonomy. For Markov coarse-graining, a microscopic kernel and a partition do not generally determine a macroscopic kernel without source occupancy π or lumpability. Joint flow aggregates exactly when occupancy is supplied. These obstructions motivate the provisional informational architecture (R, q, μ, K, π). The construction is formal and does not derive physical spacetime, calibrated geometry, gravity, or a fundamental dynamics. Supporting protocols, exact witnesses, claim-traceability files, and validation scripts are available in the associated public repository.