Unified Cross-Frame Information Convergence Architecture
Year: 2026
Type: preprint
Abstract: Observations of a common physical event may differ across sensors because the sensors operate at different elevations, coordinate frames, reference times, sampling rates, calibration states, response functions, and uncertainty regimes. Raw disagreement does not, by itself, establish a physical contradiction, a sensor failure, or the existence of a new physical phenomenon. It may arise from frame misregistration, clock offsets, distinct measurands, correlated errors, genuine event dynamics, model misspecification, or insufficient observability.This paper specifies the Unified Cross-Frame Information Convergence Architecture (UCFICA), a modular framework for diagnosing and reconciling multisource observations before reconstructing a target system state. Its operational estimation core is the Reference-Conditioned Multisource State Reconstruction Architecture (RCMSRA). The framework preserves the Multi-Elevation Spatial Observation Discrepancy (MESOD) as its primary case, the Reference-Conditioned State Reconstruction Mechanism (RCSRM) as its default mechanism, and the Cross-Dimensional State Realization Protocol (CD-SRP) as its named operational protocol.The manuscript formalizes heterogeneous measurement models, coordinate and time registration, uncertainty propagation, linear-Gaussian information-form fusion, nonlinear estimation alternatives, unknown-correlation handling, residual diagnostics, and explicit rules for unresolved or incompatible observations. It preserves the original five conceptual levels while mapping them to nine operational layers. Relativistic corrections, quantum measurement theory, and holographic or cosmological hypotheses are retained as conditional research branches rather than assumed components of every fusion problem.Conditional propositions establish the mathematical circumstances under which familiar Bayesian and linear-estimation results apply. In particular, the paper does not assume that increasing the number of sensors necessarily drives estimation covariance to zero. It defines falsifiable hypotheses, controlled simulation scenarios, empirical validation requirements, counterexamples, and a staged reproducibility protocol. No simulation or empirical result is claimed to have been obtained in this manuscript.
Author: Shan, Shamiul Hoque
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225828888
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