Revisiting Incremental Learning: A Three-Interface Diagnosis of Stability and Plasticity

Incremental learning is usually evaluated through the final classifier or LM head, but this interface does not indicate where forgetting occurs. A drop in accuracy may reflect representation degradation in the backbone, readout mismatch in the head, or both. We argue that BWT and FWT should therefore be interpreted as interface-dependent quantities rather than as single model-level properties. We study three evaluation interfaces: the sequentially trained head, a newly optimized probing classifier, and a classifier-free backbone separability diagnostic based on clustering. These interfaces answer different questions: deployed performance, recoverable information under a new readout, and representation-level separability. Across CIL experiments with encoder and decoder language backbones, plus discriminative-backbone TIL sanity checks, we find that qualitative conclusions about forgetting and plasticity change substantially with the evaluation interface. Standard heads often indicate severe forgetting, probes often suggest near-complete recoverability, and backbone diagnostics suggest intermediate representation changes and a stability–plasticity pattern. This pattern is consistent across five clustering algorithms and three clustering metrics, suggesting that it is not an artifact of a single diagnostic choice. Finally, we use Just LM-Head Tuning (JLT) as a head-only intervention to quantify recoverable performance on a fixed incrementally trained backbone. The large gap between standard and head-realigned performance suggests that many failures attributed to catastrophic forgetting are better understood as head–backbone mismatch plus partial representation drift. Our results call for reporting where forgetting is measured, not only how much forgetting is observed.