The Structured Quantitative Assessment Framework (SQAF): An Auditable Multi-Dimensional Computational Architecture for Complex Evidence Evaluation

Many real-world reasoning problems involve heterogeneous evidence, structured sequences, and constrained decision processes that cannot be fully represented by single-model inference systems. Traditional approaches often emphasize probabilistic estimation while overlooking structural compatibility and generative feasibility. This study introduces the Structured Quantitative Assessment Framework (SQAF), an auditable multi-dimensional computational architecture for evaluating complex claims under heterogeneous evidential conditions. Within SQAF, individual evidence elements are transformed through structured projection into normalized multi-dimensional representations, enabling consistent processing across three complementary analytical dimensions: an ontological dimension representing probabilistic consistency, a sequential dimension representing compatibility with natural observational sequences, and a strategic dimension representing feasibility under constrained optimal generative selection. A central design principle of SQAF is computational auditability. Evidence inputs are parameterized using credibility weights and diagnostic likelihood structures, and dimensional outputs are synthesized through predefined rules. Robustness is evaluated through constrained Global Sensitivity Analysis (GSA), allowing systematic identification of dominant evidential drivers and assessment of inference stability under admissible parameter variation. The computational behavior of SQAF is demonstrated through two structurally contrasting historical–archaeological cases. The first case exhibits strong structural inconsistency and produces a robust negative inference outcome. The second case demonstrates convergent multi-source validation and produces a robust positive inference outcome. Across both cases, results remain stable under controlled multidimensional perturbation. These findings highlight structural stability as a measurable criterion for inference validity. By integrating probabilistic reasoning, sequence-based structural validation, and optimal generative feasibility into a unified workflow, SQAF establishes a reproducible and auditable mechanism for evaluating complex claims under uncertainty.