When Edit Flows are Edit Jumps: replicating Edit Flows and EvoFlows
Antibody lead optimization calls for a small, bounded set of edits to an existing candidate: substitutions, but also insertions and deletions. Edit-based generative models are the only ones that allocate such an edit budget without fixing the edit positions, the edit count, or the output length in advance. However, the existing approaches Edit Flows and EvoFlows did not release code or complete training specifications. Here, we show that both methods follow the same underlying process -- edits firing one at a time, at learned rates, in continuous time -- the pure-jump case of generator matching over finite sequences. With EditJumps we introduce the first open implementation of this framew...
| Authors | Gabriel Bénédict, Melanie Buechler, Gerard Riera-Solà et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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