Shandong Academy of Sciences

Multimodal large reasoning models can pull sensitive information back through cross-modal associations inside intermediate reasoning, so suppressing the final answer is not enough. VKCR reframes the forget target as one designated image-to-knowledge association: it localizes the edit in a compact association-conditioned low-rank parameter subspace, then counterfactually rebinds the affected reasoning branch, weakening target-bearing or unsupported continuations while favoring evidence-grounded non-sensitive alternatives. On CLEAR-R and MLLMU-R it reduces both image-conditioned target leakage and hallucinated reassociation while preserving non-sensitive visual reasoning and permitted text-only access to the same knowledge.

Multimodal large reasoning models can pull sensitive information back through cross-modal associations inside intermediate reasoning, so suppressing the final answer is not enough. VKCR reframes the forget target as one designated image-to-knowledge association: it localizes the edit in a compact association-conditioned low-rank parameter subspace, then counterfactually rebinds the affected reasoning branch, weakening target-bearing or unsupported continuations while favoring evidence-grounded non-sensitive alternatives. On CLEAR-R and MLLMU-R it reduces both image-conditioned target leakage and hallucinated reassociation while preserving non-sensitive visual reasoning and permitted text-only access to the same knowledge.

RES-MR splits medication recommendation into two steps, first generating a checkable diagnostic reasoning chain and then adjusting the prescription to each patient's own risk boundary, reaching a 7.7% higher Jaccard than FLAME on MIMIC-III with lower DDI and RIS.

RES-MR splits medication recommendation into two steps, first generating a checkable diagnostic reasoning chain and then adjusting the prescription to each patient's own risk boundary, reaching a 7.7% higher Jaccard than FLAME on MIMIC-III with lower DDI and RIS.