当证据改变主题：用于学习路由的主题类型声明许可
When Evidence Changes the Subject: Subject-Typed Claim Licensing for Learned Routing
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对复合学习系统中证据与组件级声明不匹配的归因问题，提出主题类型声明许可方法，并在SCOPE-Routing中验证，区分声明强度与科学主题两个评估维度。
AI中文摘要：
现代学习系统日益将学习组件与搜索、修复或外部求解器相结合。基准测试通常衡量由此产生的端到端系统，而科学声明可能仅涉及其中一个组件，这造成了归因问题：证据可能无法支持所请求的组件级声明，但仍能支持关于更大系统的积极结论。现有的证据到声明方法主要校准声明强度。我们认为复合系统需要第二个维度：科学主题。我们通过主题类型声明许可来解决这个问题，该许可将关于所请求主题的较弱结论与关于另一主题的积极但不可替代的信用区分开来。我们在SCOPE-Routing中实例化了这一思想，用于偏好条件多图路由。非作者可复现地应用所声明的语义；留出审查产生的相对于参考的向上偏差少于非结构化审查，而与强证据检查清单的差异仍未解决；一项受控路由研究表明，得分最优和符合声明的方法可能不同，同时保留了有效的混合系统信用。这些结果促使将声明强度和科学主题视为基于证据评估的独立维度。
英文摘要：
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.
