Abstract
Abstract: This study investigates an explainable quantum-logical multi-agent system (QL-MAS) for autonomous scientific discovery in high-dimensional research landscapes. The central methodological problem is that modern discovery settings combine large numbers of variables, heterogeneous evidence types, and partially conflicting reasoning signals, yet conventional single-model pipelines often compress this complexity into opaque rankings. The proposed framework addresses this limitation by treating candidate scientific hypotheses as quantum-logical superpositions of evidence generated by three complementary agents: an evidence agent based on mutual information, a structure agent based on principal-component loadings, and a rule agent based on interpretable decision structures. The resulting architecture does not claim physical quantum computation; rather, it uses quantum-logical amplitude composition as a mathematically disciplined way to represent concurrent support, interference, and consensus across agents. Empirical evaluation is conducted on three real public datasets that approximate heterogeneous scientific discovery landscapes: the Breast Cancer Wisconsin (Diagnostic) dataset (569 observations, 30 variables), the Wine Recognition dataset (178 observations, 13 variables), and the Diabetes dataset used in the least-angle-regression literature (442 observations, 10 baseline variables). These corpora were selected because they are non-synthetic, scientifically grounded, and sufficiently high-dimensional to test feature prioritization, interpretability, and cross-domain transfer. Across the three datasets, the proposed system identifies compact sets of high-salience hypotheses while preserving strong downstream predictive utility. The top-five features selected by QL-MAS attain cross-validated scores of 0.965 on breast cancer classification, 0.955 on wine classification, and 0.465 R² on diabetes progression, remaining close to full-feature baselines while offering substantially more interpretable evidence paths. The results indicate that explainable multi-agent discovery is strongest when high predictive relevance and rule-based consistency converge, and weakest when latent structure is diffuse or poorly aligned with supervised objectives. The analysis therefore supports the conclusion that autonomous discovery systems in research environments should not be assessed solely by predictive accuracy, but by the transparency of their evidential decomposition and by the stability of agreement across reasoning agents. The paper contributes a theoretically coherent framework, a real-data evaluation protocol, and a discussion of limitations, governance, and future directions for autonomous scientific reasoning systems. Keywords: autonomous scientific discovery; explainable AI; multi-agent systems; quantum logic; high-dimensional data; interpretable machine learning; research landscapes
