The Atom-Cloud-Fact Epistemological Pipeline: From Financial Document Processing to Brand Perception Modeling

This paper argues that the atom-cloud-fact pipeline constitutes an epistemological architecture demonstrably transferable across at least two structurally distinct domains. The pipeline models knowledge formation through three formally specified stages: atomic observation (typed, source-bound data extraction), probabilistic cloud formation (weighted multi-dimensional clustering), and fact collapse (threshold-based crystallization with full re-collapse on new evidence). Four propositions are developed: (P1) the pipeline’s external-corroboration requirement structurally reduces confirmation bias relative to single-source methods; (P2) multi-model replication produces more robust assessments because framework-driven conclusions are replicable while model-specific artifacts are not – conditioned on architectural divergence and structural convergence, with human-subject validation required; (P3) three-stage epistemic separation prevents category errors conflating brand signals, perception clouds, and brand convictions; and (P4) cross-model disagreement is diagnostic of evidential ambiguity, not model failure. The architecture originated in financial document processing; its seven principles transfer with parametric extension to brand perception modeling, producing Spectral Brand Theory (SBT). Existing approaches – Bayesian updating, AGM revision, Dempster-Shafer theory, and symbolic expert systems – each fail structurally for at least one of four requirements: path-independence, observer heterogeneity, epistemic separation, or linguistic implementability. The cross-domain transfer supports the claim that the pipeline captures a general structure of knowledge formation from heterogeneous observation. Includes zharnikov-2026b-alibi-epistemology.yaml (Paper Spec v0.1.0) – a machine-readable specification of the paper's claims, assumptions, and dependencies. The paper's full machine-first bundle (the SPINE claim/dependency graph and the ONTOLOGY term module) lives in the public repository; see https://github.com/spectralbranding/paper-spec for the standard. This PDF is generated programmatically from that machine-first source under a research-as-repository model.