Evidence Provenance Routing for Robust Decision Making under Predictor Disagreement

Modern decision pipelines increasingly rely on heterogeneous predictors trained over distinct evidence sources. While these models often achieve comparable aggregate accuracy, they frequently produce conflicting predictions on individual instances—leading to brittle downstream decisions under non-stationary conditions. We show that a fundamental driver of this disagreement is evidence provenance: different predictors observe distinct evidence scopes whose relevance shifts dynamically across environments. To address this, we introduce Evidence Provenance Routing (EPR), a framework that maintains source-specific predictors as explicit decision anchors and dynamically allocates source authority during prediction conflicts. Unlike prior routing methods relying on static confidence metrics or historical accuracy, EPR evaluates authority using context-level evidence available at decision time. Specifically, EPR decouples routing into two stages: (1) Observable State–Decision Alignment (SDA), which identifies candidate source switches based on observable context, and (2) Disagreement Representation & Assessment, where a frozen language model encodes conflict semantics and lightweight probes validate stable route updates. Crucially, the final decision is executed by the selected source anchor, preserving domain-specific calibration rather than delegating task generation to the LLM. Evaluated across three non-stationary financial decision benchmarks and a legal outcome-prediction dataset, EPR consistently outperforms prediction aggregation, direct LLM reasoning, decision-focused learning, and modern router baselines. Finally, we provide a decision-theoretic analysis characterizing the value of context-dependent authority, bounding the decision cost of provenance compression, and decomposing routing risk across information, proposal and assessment stages.