  Antibody design plays a pivotal role in advancing therapeutics. Although deep
learning has made rapid progress in this field, existing methods jointly
generate antibody sequences and structures, limiting task-specific
optimization. In response, we propose an antibody sequence-structure decoupling
(ASSD) framework, which separates sequence generation and structure prediction.
Although our approach is simple, such a decoupling strategy has been overlooked
in previous works. We also find that the widely used non-autoregressive
generators promote sequences with overly repeating tokens. Such sequences are
both out-of-distribution and prone to undesirable developability properties
that can trigger harmful immune responses in patients. To resolve this, we
introduce a composition-based objective that allows an efficient trade-off
between high performance and low token repetition. Our results demonstrate that
ASSD consistently outperforms existing antibody design models, while the
composition-based objective successfully mitigates token repetition of
non-autoregressive models. Our code is available at
\url{https://github.com/lkny123/ASSD_public}.
