STABLE REWEIGHTING FOR HETEROGENEOUS EVIDENCE FUSION UNDER SOURCE EXCLUSION

How much can excluding a single evidence source change a fused prediction, even when the candidate models remain unchanged? This question is formalized as source-exclusion stability for fixed-support evidence fusion: controlling the largest shift induced in the fused predictive distribution when any one source’s complete evidence contribution is removed. We propose Source-Exclusion Stable Reweighting (SESR), a reweighting objective that, to our knowledge, is the first to directly penalize the largest, rather than average, squared displacement between the all-source mixture weights and each leave-one-source-out mixture weight, jointly optimized on a shared candidate support. We prove a deterministic bound connecting this worst-case weight displacement to predictive total-variation shift, establishing weight-space coupling as a principled surrogate for prediction stability.On five weak-supervision datasets, SESR reduces worst-case prediction shift by 18.9–31.9% relative to Independent Reweighting (IR) and by 15.3–25.9% relative to Average Coupling (AC) across all dataset means, with corresponding weight shift reductions and broadly preserved classification accuracy. These results support source-exclusion stability as an explicit, optimizable objective for heterogeneous evidence fusion.