Beyond Semantic Alignment: Geometric Incomparability in Multi-Oracle Soft Fusion

Many decision-level fusion methods implicitly assume that semantically aligned soft outputs can be directly aggregated once they share the same label simplex. However, this assumption can fail even under identical label semantics, as oracle-specific differences in confidence sharpness, boundary uncertainty, and tail-mass allocation may place soft outputs in incompatible probability geometries and bias direct soft fusion. To address this issue, we formalize this failure mode as geometric incomparability and propose a pre-fusion comparability recovery framework, which learns a shared consensus through constrained oracle-specific rectifiers on the probability simplex. The framework includes a closed-form variant with frozen geometry descriptors and an alternating-refinement variant with consensus-dependent descriptors. Theoretically, we analyze the rectifier family, prove exact recovery under matched radial distortions, characterize the bias of direct fusion, and establish stable recovery under approximate mismatch. Experiments on controlled synthetic stress tests and real black-box decision-level settings show that comparability recovery consistently reduces fusion bias and improves reliability over direct aggregation. These results indicate that geometric comparability is a necessary condition for reliable soft-output fusion. The code is available at \url{https://anonymous.4open.science/r/CFCR-ACR-B8F1}.