Cross-Domain Evidence-Constrained State Reconstruction Theory
Year: 2026
Type: preprint
Abstract: Heterogeneous evidence systems frequently combine observations that differ in measurement process, provenance, semantic representation, reliability, temporal coverage, spatial resolution, and statistical dependence. Naive aggregation can consequently amplify duplicated sources, conceal systematic bias, misrepresent uncertainty, and force consensus across claims that are not genuinely comparable.This manuscript proposes Cross-Domain Evidence-Constrained State Reconstruction (CDECSR), a research architecture whose default mechanism is Provenance-Aware, Uncertainty-Preserving State Reconstruction (PAUSR). The architecture preserves four macro-stages: taxonomic and semantic anchoring; error, dependence, and reliability parameterization: triangulation, robust fusion, and contradiction management; and latent-state estimation, validation, and controlled revision. These stages are operationalized through fourteen layers.The formal framework distinguishes the underlying target state from observations and model-dependent estimates. It represents heterogeneous observations through source-specific measurement models, accounts for shared provenance and correlated errors, and treats source reliability as a conditional quantity rather than a direct measure of truth. The proposed output is a state estimate or posterior distribution accompanied by uncertainty, provenance, unresolved contradictions, assumptions, validation results, and a reproducibility record.The architecture is specified through the Conflicting Reports in an Environmental Crisis (CREC), a structured primary case in which field reports and other observations may disagree about event location, timing, intensity, extent, and progression. The manuscript also defines a falsifiable research program, conditional mathematical results, counterexamples, simulation requirements, validation protocols, and a fourteen-step pathway toward a research paper.
Author: Shan, Shamiul Hoque
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225816948
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