# Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision

arXiv: https://arxiv.org/abs/2604.09537 · HTML: https://mlsanity.com/paper/2604.09537/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 68 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://mlsanity.com/paper/1909.12434/) (2019)
- [Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.09712/) (2022)
- [CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formats](https://mlsanity.com/paper/2405.19538/) (2024)
- [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://mlsanity.com/paper/2003.10555/) (2020)
- [Agentic retrieval-augmented reasoning reshapes collective reliability under model variability in radiology question answering](https://mlsanity.com/paper/2603.06271/) (2026)

## Built on by (most cited first, 3 of 3)

- [Safety and accuracy follow different scaling laws in clinical large language models](https://mlsanity.com/paper/2605.04039/) (2026)
- [The strength of clinical evidence is recoverable from language model representations but not from their stated grades](https://mlsanity.com/paper/2606.29034/) (2026)
- [Cross-modal linkage risk in clinical vision-language models](https://mlsanity.com/paper/2606.02276/) (2026)

## Abstract (arXiv)

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 outperforms both case-only and evidence-only baselines, remains strong under correct evidence, and collapses when evidence is removed or swapped, indicating genuine evidence dependence. This behavior transfers across unseen evidence articles and an external case distribution, though performance degrades under evidence-source shift and remains sensitive to backbone choice. Overall, the results suggest that a major bottleneck in evidence grounding is not only model capacity, but the lack of supervision that encodes the causal role of evidence.