Test-Time Scaling for Reliable Image Editing via Contract-Guided Verification

Test-time scaling has substantially improved instruction-guided image editing by generating and comparing multiple candidate edits. However, ranking candidates by a unified evaluation score may cause highly ranked outputs to omit requested changes or alter content that should remain unchanged. In this work, we propose Contract-Guided Test-Time Scaling (CG-TTS), a training-free approach that uses explicit edit requirements to guide generation, verification, and repair with in place image-editing models. Specifically, for each instruction requirement, CG-TTS first constructs a contract specifying the editing target, requested changes, allowed regions, and preserved content, together with checks used to verify the given requirements. Then, we generate diverse candidate edits using a frozen editor equipped with a source-memory routing mechanism, which routes cached source-image features according to the contract. A staged verifier is further used to check spatial changes and operation-specific requirements, accepting candidates only when all active mandatory checks pass. If no candidates pass the verifier, CG-TTS uses verifier feedback to obtain diagnosis types that guide failure-conditioned repair, and the repaired candidates are verified against the same contract. Finally, CG-TTS selects the highest-ranked passing candidate as the output, while if none passes, any permitted fallback retains a non-pass status. Experimental results demonstrate that our method consistently improves performance across strong baseline models and outperforms state-of-the-art methods under the same computational budget. Code is available at https://anonymous.4open.science/r/s-E968.