Qilu University of Technology

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.

As Earth-system AI models approach kilometre scales, halving grid spacing quadruples the grid-point count and the end-to-end training cost. One2N splits resolution refinement into successive increments: at each level the complete parent predictor is frozen and only a cross-resolution residual correction module is trained, so the parent's activations and optimizer states are never retained. One refinement level on the NeuralGCM-0.7° baseline yields One2N-0.07°, trained for about four weeks on a single 80 GB A800 GPU; against HRES-0.07° analyses it keeps the parent's tropospheric skill while reducing upper-stratospheric errors and resolving finer structure.

As Earth-system AI models approach kilometre scales, halving grid spacing quadruples the grid-point count and the end-to-end training cost. One2N splits resolution refinement into successive increments: at each level the complete parent predictor is frozen and only a cross-resolution residual correction module is trained, so the parent's activations and optimizer states are never retained. One refinement level on the NeuralGCM-0.7° baseline yields One2N-0.07°, trained for about four weeks on a single 80 GB A800 GPU; against HRES-0.07° analyses it keeps the parent's tropospheric skill while reducing upper-stratospheric errors and resolving finer structure.

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.

Remote sensing super-resolution previously injected low-resolution observations as dense spatial conditions into a diffusion Transformer, so small foreground objects were easily diluted by dominant backgrounds. MOSAIC organizes degraded observations into compact object-level and layout-level structural slots, fuses them across resolutions, and retrieves recurring structural priors from a shared prototype memory, then injects them into a frozen DiT4SR backbone through lightweight residual modulation. On UCMerced, AID and RSSCN7, PSNR is best at both scales, exceeding DiT4SR by 2.03 dB on UCMerced, 4.54 dB on AID and 1.49 dB on RSSCN7, with the lowest LPIPS on RSSCN7 and AID.

Remote sensing super-resolution previously injected low-resolution observations as dense spatial conditions into a diffusion Transformer, so small foreground objects were easily diluted by dominant backgrounds. MOSAIC organizes degraded observations into compact object-level and layout-level structural slots, fuses them across resolutions, and retrieves recurring structural priors from a shared prototype memory, then injects them into a frozen DiT4SR backbone through lightweight residual modulation. On UCMerced, AID and RSSCN7, PSNR is best at both scales, exceeding DiT4SR by 2.03 dB on UCMerced, 4.54 dB on AID and 1.49 dB on RSSCN7, with the lowest LPIPS on RSSCN7 and AID.