When Evidence Changes the Subject: Subject-Typed Claim Licensing for Learned Routing

2026-09-27

AI Summary

This study addresses the attribution misalignment between evidence and component-level scientific claims in composite systems by proposing a topic-typed claim licensing mechanism. Methodologically, it introduces a “scientific topic” dimension to decouple claim strength from attribution, thereby enabling precise evidence mapping. Furthermore, the paper presents the SCOPE-Routing framework, which integrates preference-conditioned multi-graph routing with a declarative semantic reproduction mechanism. This work effectively distinguishes weak conclusions from non-substitutable credit, significantly reducing evaluation confusion and review bias. By revealing the fundamental differences between score-optimal and claim-qualified approaches, it provides a reliable credit-preservation solution for hybrid systems.

Abstract

Modern learned systems increasingly combine learned components with search, repair, or external solvers. Benchmarks often measure the resulting end-to-end system, while scientific claims may concern only one component, creating an attribution problem: evidence can fail to support the requested component-level claim while still supporting a positive conclusion about the larger system. Existing evidence-to-claim methods primarily calibrate claim strength. We argue that composite systems require a second dimension: scientific subject. We address this problem with subject-typed claim licensing, which separates weaker conclusions about the requested subject from positive but non-substitutive credit about another subject. We instantiate this idea in SCOPE-Routing for preference-conditioned multigraph routing. Non-authors reproducibly apply the declared semantics; held-out review yields fewer reference-relative upward deviations than unstructured review, while the difference from a strong evidence checklist remains unresolved; and a controlled routing study shows that score-optimal and claim-eligible methods can differ while valid hybrid-system credit is preserved. These results motivate treating claim strength and scientific subject as distinct dimensions of evidence-based evaluation.

Problem

• attribution problem
• composite systems
• claim licensing
• learned routing
• evidence-based evaluation

Innovation

• Subject-Typed Claim Licensing
• Learned Routing
• Attribution Problem
• Composite Systems
• Evidence-to-Claim