This work develops a relational and inference-first framework for understanding clock comparison, synchronization, and global consistency in physical systems. Rather than treating clock comparison as a primitive operation or as a purely dynamical problem, the paper approaches it as an inverse problem constrained by finite data, protocol dependence, and representational freedom. From this perspective, many familiar assumptions about global time, strict synchronization, and compositional closure turn out not to be generic properties of physical systems, but special cases that require additional structure. At the operational level, a clock is a local physical system that can perform control actions (such as emitting signals) and register reception events. It does not have direct access to “sent timestamps” of distant clocks, nor to a global time parameter. Any relation between clocks must therefore be inferred from finite, noisy, and protocol-dependent signal exchanges. This inferential character is not a technical inconvenience but a structural feature of clock comparison itself. When taken seriously, it leads to comparison maps that exhibit memory, dependence on past interactions, and sensitivity to representation choices. A central consequence of this inferential viewpoint is that clock comparison maps do not, in general, compose strictly. Comparing clock A to clock C directly does not necessarily yield the same result as composing the comparison of A to B with that of B to C, even in flat spacetime and within a single local laboratory region. This failure of strict composition gives rise to invariant loop defects: mismatches accumulated when relational information is transported around closed cycles of contexts. These defects are not errors, noise, or curvature effects; they are structural and persist under changes of representation. To capture this structure correctly, the paper introduces the notion of de-strictification. De-strictification means abandoning the assumption that all clock relations can be embedded into a single global time variable or representation. Instead, clock networks are described using a weak (groupoid or higher-categorical) structure in which composition holds only up to coherent equivalence. In this setting, loop defects appear naturally as operational holonomy, analogous in role (though not in nature) to holonomy in gauge theory. Building on this, the paper introduces the key concept of a Contextual Malament–Hogarth (Context–MH) obstruction. This notion abstracts the structural core of Malament–Hogarth spacetimes—namely, the coexistence of unbounded internal process with finite access—into a purely relational and operational setting. In the contextual case, the relevant “infinity” is not infinite proper time or exotic spacetime geometry, but unbounded compositional depth: the fact that relational structure continues to accumulate under repeated composition and refinement. At the same time, finite observers can access stable, gauge-invariant summaries of this structure. Crucially, Context–MH obstruction does not imply any causal pathology, non-global hyperbolicity, infinite precision, or hypercomputation. Gauge freedom is reinterpreted in this framework as representational non-uniqueness rather than physical redundancy. Different gauges correspond to different ways of representing the same relational content. Retaining multiple gauges simultaneously leads naturally to a groupoid formulation in which gauge transformations are explicit morphisms rather than eliminations. Within this multi-gauge setting, the paper defines gauge side-quests: optimizations over degrees of freedom that are not fixed by the task specification (the invariant relational content). These side-quests can shorten effective memory tails, improve numerical conditioning, and make inference feasible, while provably preserving all invariant obstructions such as loop holonomy and Context–MH structure. Gauge side-quests therefore enhance how relational information is accessed, without changing what information is invariantly present. To organize relational structure across increasing compositional depth, the paper develops a higher-categorical renormalization group. Unlike conventional renormalization, where scale is associated with spatial distance or energy, the relevant notion of scale here is relational depth: how many layers of contextual comparison are composed. Coarse-graining acts to suppress representational detail while preserving invariant structure. Because strict fixed points generally do not exist in the presence of Context–MH obstruction, the renormalization flow yields fixed classes rather than fixed objects. This leads to the concept of universal structure renormalization, which produces a hierarchy of invariant summaries. Each level in the hierarchy captures more relational information than the previous one, while remaining accessible to finite observers using finite data and finite resources. The hierarchy expands what can be stably known about the system, but it does not provide access to arbitrary predicates of infinite relational structure. In this way, the framework remains fully compatible with standard causality and the Church–Turing thesis. Conceptually, the results support a unified interpretation of several phenomena that often appear separately in physics, computation, and complex systems. Computation appears as the extraction of invariant structure rather than execution of explicit algorithms. Learning appears as stabilization of inference under repeated interaction rather than acquisition of semantic content. Apparent competence or “side-quest” behavior arises from optimization over underdetermined structure subject to global constraints, rather than from hidden objectives or centralized control. The framework developed here is relevant to foundational questions about time, synchronization, gauge structure, and renormalization, and may inform the analysis of experimental clock networks and other distributed physical systems where inference, rather than dynamics alone, limits global consistency. At the same time, it remains deliberately conservative in its claims, introducing no exotic spacetime assumptions, no non-classical computation, and no cognitive postulates. Its contribution lies in clarifying how rich, competence-like behavior can arise from relational structure while remaining fully within established physical and mathematical principles.
Andrei T Patrascu (Thu,) studied this question.