AI RESEARCH
LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
arXiv CS.AI
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ArXi:2605.11368v1 Announce Type: cross We study the application of recent Edit Flows for inference-time reward control for DNA sequence generation. Unlike most reward-guided DNA generation frameworks, which operate on fixed-length sequence spaces, Edit Flows have a potential to generate variable-length DNA through biologically plausible insertion, deletion, and substitution operations. In particular, we propose Local Perturbation Discrete Programming (LPDP), a