SIFT: Boundary-Free Online Adaptation via Semantic Isolation and Functional Testing

Boundary-free online continual learning aims to adapt large language models to a stream of evolving data without access to task identities or boundaries. Without this supervision, the learner must decide from the stream alone when incoming data can safely reuse existing adaptation capacity and when it requires separate capacity. Sharing one set of adaptation parameters across the stream causes interference and forgetting, whereas allocating separate parameters typically relies on externally provided task boundaries and can increase parameter counts unnecessarily. We present SIFT (Semantic Isolation and Functional Testing), a framework for boundary-free online adaptation that keeps the language model backbone frozen and organizes the stream into mature semantic modules and provisional modules. Because nearby regions can still require conflicting updates, semantic proximity alone cannot certify that adaptation capacity can be shared, so SIFT defers the decision until functional evidence is available. It routes examples to mature modules by cosine distance, isolates uncertain ones into provisional modules that inherit a parent prompt but adapt independently, and consolidates a provisional module only when a radius-based compatibility check and a sentinel-based functional validation agree. We conduct extensive experiments using four instruction-tuned backbones from the Llama and Qwen families, spanning 0.6B to 7B parameters. SIFT achieves up to 21.96% relative improvement in final accuracy over the strongest baseline while substantially reducing forgetting.