CHEC: Counterfactual Low-Rank Adaptation with Historical Evidence Correction for Continual Missing Modality Learning

Continual Missing Modality Learning (CMML) is important for multimodal systems that must continually acquire new knowledge while remaining reliable under intermittent modality absence. However, existing methods still face two limitations that require further improvement in performance: (1) they mainly compensate for missing-side information, without explicitly adapting the surviving modality after its cross-modal context changes; and (2) preserving historical knowledge does not ensure that its contribution to the current prediction is supported by the current input. To address these problems, this paper proposes CHEC, a novel CMML framework combining Counterfactual Low-Rank Adaptation with Historical Evidence Correction. During training, CHEC adds a controller conditioned on the current sample and modality availability to LoRA, allowing the transformation applied to the surviving modality to adapt to each input. It further introduces Utility-Gated Counterfactual Functional Correction (UG-CFC), which projects the functional difference between paired Complete and Missing views into a counterfactual correction attainable in the Missing-view rank space, and retains it only when the correction is predicted to benefit classification. This resolves the mismatch between the desired functional change and what the Missing representation can realize, while filtering harmful corrections without increasing LoRA rank. During inference, Historical Evidence Correction (HEC) replaces task-level hard routing with class-wise evidence correction. It retains all learned classes and calibrates historical evidence according to support from the current input, avoiding both premature class exclusion and unsupported contributions from historical knowledge. Across two existing CMML benchmarks, CHEC achieves the state-of-the-art performance among six baselines in all 30 settings. Further analyses show relative reductions of 14.0–40.4% in the Complete–Missing functional gap and 27.4–32.2% in the final historical-class error rate.