MuLoRA: התאמה ספקטרלית מאוזנת

תקציר מקורי באנגליתarXiv:2610.02283v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) provides a parameter-efficient approach to continual learning, but its nominal rank can conceal a loss of effective adaptation capacity. We identify \emph{spectral plasticity collapse}: during sequential adaptation, update energy becomes concentrated in a small subset of singular modes, leaving much of the available low-rank space underutilized. This exposes a limitation of interference avoidance alone: protecting historical representations does not ensure that the remaining adaptation capacity is responsive to new tasks or effectively utilized. To address this problem, we propose \texttt{MuLoRA}, which jointly controls capacity allocation and utilization. First, historical whitening identifies input directions with