Do Language Models Revise the Right Conclusions? Diagnosing and Training Evidence-Contingent Reasoning

Aggregate answer scores cannot tell whether a language model revises the right conclusions when its evidence changes. We study evidence-contingent reasoning (ECR): withdrawing conclusions that lose support, preserving unaffected content, and retaining conclusions that have a valid alternative proof in the target world. Our evaluation suite, EviPair, links audited evidence worlds and measures Change, Preserve, and Switch on fixed, model-independent eligibility sets. On MuSiQue-Full, FEVER, and QAMPARI, reinforcement learning with only node-local semantic and evidence rewards improves answering but lowers preservation on every task, and development-set endpoint matching still leaves substantial response differences on the full test graphs. We instantiate structured intervention training with Paired Transition Policy Optimization (PTPO), which credits each node with the marginal return of its incident intervention edges while reusing independently sampled world trajectories. With Forced Switch edges (which invalidate every source-world proof of a conclusion while retaining alternative support) withheld from reinforcement learning, macro SwitchAcc on FEVER and QAMPARI rises from 17.48 after supervised finetuning (SFT) to 33.42. The gain over the strongest prior-method baseline, CRPO-Evidence, is 4.28 points (95% paired root-clustered interval [2.21, 6.35]); the Change and Preserve differences are not significant. The clearest benefit of structured intervention training over CRPO-Evidence thus lies in alternative-proof adaptation, and local revision and proof adaptation are best evaluated as separate targets.