RAM-CIL: Reliability-Aware Multimodal Class-Incremental Learning under Modality Reliability Shift

Experience replay preserves historical training examples, but their observed modalities need not remain reliable learning signals. We study audio-text class-incremental intent recognition under controlled changes to current and replay observations. Holding current inputs clean, replacing historical audio and transcripts with paired shifted versions reduces old-task retention by 15.90 percentage points. We introduce Reliability-Aware Multimodal Class-Incremental Learning (RAM-CIL), which combines a base retrieval score with rank-normalized, label-conditioned predictive support from independent modality classifiers, without changing replay features or the learning objective. The results reveal a protocol-dependent separation between predictive support, retention, and new-task acquisition. Over a complete six-task run, additive reliability correction raises final Macro-F1 while reducing old-task retention. From a shared task-three checkpoint, the same correction instead improves retention. A separate, privileged diagnostic that filters MIR candidates using paired degradation scores improves new-task acquisition by 6.91 points with a 0.21-point retention decrease. These findings establish neither uniform forgetting reduction nor a universal plasticity benefit. They show that predictive reliability is insufficient as a proxy for replay utility, and motivate evaluating stability and plasticity separately under explicitly matched replay protocols.