Minimum-Sufficient Selective Control for Instruction-Based Image Editing

Instruction-based image editing handles many simple requests well, but difficult edits still expose a control-allocation problem: weak intervention often under-edits hard cases, while uniformly strong intervention increases spillover and cost. We cast editing as minimum-sufficient control allocation over a locked bounded-cost intervention family with explicit end-to-end efficiency accounting. We keep this intervention family fixed and learn a lightweight controller from deployment-time observables. The policy first decides whether to leave the temporal anchor, then selects the promoted temporal horizon, and finally applies deterministic localization to the escalated branch. On the locked MagicBrush and PIE-Bench++ benchmarks, the full policy FO improves over the temporal-only anchor TO from 0.7607 to 0.8481 edit success and from 22.9583 to 24.7291 composite score while staying within the same efficiency envelope, with sub-second latency and low VRAM. Matched-budget controls, multi-seed confidence intervals, abstraction-transfer tests, and oracle diagnostics support that the gain comes from better allocation rather than a uniformly heavier editing path.