[2604.09537] Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision

Computer Science > Computation and Language arXiv:2604.09537 (cs) [Submitted on 10 Apr 2026] Title:Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision Authors:Soroosh Tayebi Arasteh, Mehdi Joodaki, Mahshad Lotfinia, Sven Nebelung, Daniel Truhn View a PDF of the paper titled Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision, by Soroosh Tayebi Arasteh and 4 other authors View PDF HTML (experimental) Abstract:Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the target claim. In practice, this often fails because supervision is weak, evidence is only loosely tied to the claim, and evaluation does not test evidence dependence directly. We introduce case-grounded evidence verification, a general framework in which a model receives a local case context, external evidence, and a structured claim, and must decide whether the evidence supports the claim for that case. Our key contribution is a supervision construction procedure that generates explicit support examples together with semantically controlled non-support examples, including counterfactual wrong-state and topic-related negatives, without manual evidence annotation. We instantiate the framework in radiology and train a standard verifier on the resulting support task. The learned verifier substantially outperfo...