Post-training compression of large language model attention is usually framed as a set of independent matrix approximation problems, according to the abstract of a new arXiv paper. That framing, the authors argue, ignores two things: the shared structure that exists among attention projections, and the representation shift introduced by earlier compression steps. In other words, compressing one part of the model can change the activations seen by later parts, and treating each matrix in isolation misses those dependencies.
The paper proposes a method called Sequential Functional Structured Tucker Compression to address these issues. The abstract, however, is truncated mid-sentence, so the available text does not describe how the method works or present any experimental results. The title and motivation are all that can be assessed from the source.
Because the source only establishes the problem statement and the name of the proposed approach, the significance of the work cannot be fully evaluated from this abstract alone. If the method delivers on its motivation, it would represent a move away from independent matrix approximation toward a more holistic view of attention compression, but the evidence for that is not in the provided text.