Unifying Task Reformulation Pipelines into an Image Editing Harness

Recent work has shown that external pipelines can substantially improve image editors by reorganizing how editing requests are processed. However, existing pipelines are typically designed around specific weaknesses or failure modes. We collect and devise several pipelines, observe their behavior on five benchmarks, and identify three characteristic behaviors. Their benefits are request-dependent and complementary; unnecessary interventions can degrade otherwise successful edits; and naively combining multiple pipeline guidances can introduce conflicts rather than accumulate their benefits. Motivated by these observations, we propose a unified image-editing harness that selectively configures task reformulation for each request. Specifically, a two-stage router selects an appropriate reformulation, on-demand skills provide pipeline-specific guidance, and shared execution controls manage visual context, tool interventions, and recovery. We evaluate the resulting system with FLUX.2-klein-9B as the primary editor across five image-editing benchmarks, against a broad set of strong baselines, together with module ablations and cost analysis. Without updating the primary editor's parameters, our harness substantially improves its editing performance and approaches the quality of Nano Banana Pro at a lower inference cost.