Recursive Semantic Orientation

Complex systems often exceed the working memory of an investigator or agent because their components, relations, states, boundaries, and unknowns cannot be held together at once. Recursive Semantic Orientation (RSO) is a proposed method for acquiring a usable position within that complexity before pursuing exhaustive detail. The source method moves from hypothesis to coarse semantic partition, recursive refinement, verification, pattern discovery, focused search, and repetition. This paper preserves that architecture while narrowing its strongest claims. Complexity is not exclusively relational, all inquiry does not begin with an explicit hypothesis, and decomposition does not guarantee a simple or unique explanation. Partitions can conceal cross-cutting relations; familiar patterns can create confirmation bias; deferred retrieval can postpone critical evidence. RSO is therefore specified as a governed, graph-aware orientation loop. It maintains hypotheses, partitions, unknowns, evidence, questions, budgets, countermodels, and stopping conditions. It supports observation-led as well as hypothesis-led entry, searches for disconfirming evidence, and distinguishes a useful working model from truth, decision authority, or implementation readiness.