Extension of alignment-drift corrections to other continuous-memory tasks

Investigate whether the alignment-drift mechanism and its runtime and training corrections extend to other continuous-memory tasks beyond single-model speculative decoding for automatic speech recognition.

Background

The paper demonstrates alignment drift and evaluates two corrective strategies—verification-attention runtime correction and AnchorDraft training supervision—in single-model speculative ASR. However, the authors restrict their empirical claims to the tested ASR architectures, target scales, and serving conditions. They explicitly identify generalization to other continuous-memory tasks as unresolved, leaving open whether the same position-tracking failure and correction principles apply beyond ASR.