CardBench-X: Benchmarking Memory-Guided Bidirectional Visual Evolution

Existing image-generation benchmarks mainly evaluate prompt following, image quality, or the injection of new semantic knowledge. They rarely test whether an agent can separate visual knowledge that should remain invariant from knowledge that should be added or removed during an image transformation. We introduce CardBench-X, a benchmark for memory-guided bidirectional visual evolution. Given an input subject, a source tier, and a target tier, an agent must transform the image forward or backward across a discrete visual hier- archy while preserving identity-defining content. The bench- mark evaluates target-tier realization, knowledge retention, knowledge injection, reversibility, and transfer of transforma- tion experience across subjects and styles. We also provide CardHarness, a trace-driven baseline that stores success- ful and failed transformations, localizes over-design, under- design, identity drift, and stagnation, and retrieves validated repairs for later cases. CardBench-X offers a controlled envi- ronment for studying whether visual agents merely add more detail or actually learn reusable, direction-aware transforma- tion knowledge.