Latent-to-Legal Gradient Planning over Dynamically Revealed CRISPR Edit Graphs

Every intended genome edit changes the sequence that defines which subsequent edits are executable. On RegEdit, a frozen set of 50 regulatory tasks, the median Jaccard overlap between consecutive action sets is 0.10; in 80% of tasks the tra- jectory selects a later action that did not exist at the root—one that a root-defined program could not have named. We formulate this as planning with state-dependent executable action identities: a root-fixed program retains the original candidate list, while a dynamic program regenerates the executable set after every transition. In the native comparison the dynamic regime produces a higher model-predicted endpoint on all 50 tasks— yet under exposure-matched conditions the difference vanishes (bootstrap 95% CI crossing zero). The action space changes dramatically; the current search does not exploit them. The gap between reachable and reached defines the open problem: converting action-space turnover into utility. Within this regime, L2L-GP achieves the highest median gain at every tested native query scale.