Timing of memory integration shapes representations of causal uncertainty

Two questions have largely been studied separately in hippocampal research: how uncertainty over hidden structure is represented and when episodic memories are integrated relative to a query. We bring them together by extending the Episodic Spatial World Model (ESWM) framework from spatial mapping to inference over latent causal structure. We compare matched architectural instantiations of integrative encoding, which compresses ambiguous evidence before a query is known, and retrieval-based inference, which combines stored episodes after the query identifies the relevant causal relation. DeepMind's Symbolic Alchemy provides an enumerable space of causal worlds, allowing us to compute the exact posterior over structures and the posterior predictive for every query. This provides a normative reference for comparing the beliefs expressed by models with nearly indistinguishable behavioral accuracy. At sufficient capacity, the two modes achieve nearly indistinguishable behavioral performance while relying on different internal computations and uncertainty representations: retrieval-based-inference models preserve more separable memories, preferentially retrieve query-relevant evidence, and represent uncertainty more faithfully, whereas integrative-encoding models form representations that generalize better across queries. As a single representation is required to support more queries, models smoothly shift from query-specific toward reusable, integrative-encoding-like representations. These results characterize integrative encoding and retrieval-based inference as endpoints of a trade-off between query-specific inference and representational reuse.