AI

Reasoning-Informed Visual Editing

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 03:18:24 UTC.

Briefings

Researchers introduced RISEBench, the first benchmark for Reasoning-Informed Visual Editing, and RISEBench++, a hierarchical version with 65 fine-grained task types. They expanded the scope to include multi-image conditioning and 1000 human-annotated test cases in English and Chinese. The evaluation framework uses human judges and an LMM-as-a-judge approach to assess instruction reasoning, appearance consistency, and visual plausibility. The study evaluated 58 visual editing approaches, including 34 open-source and 19 closed-source models, with the strongest performing model achieving only 56.6% accuracy.

Key points

• Researchers introduced RISEBench, the first benchmark for evaluating Reasoning-Informed Visual Editing (RISE), and extended it to RISEBench++ with a hierarchical scheme covering six reasoning dimensions.
• The benchmark includes 1000 human-annotated test cases in English and Chinese, with 65 fine-grained task types and 12 subcategories.

Context

Large Multi-modality Models (LMMs) have made progress in visual understanding but face challenges in visual editing, specifically in following complex instructions and preserving appearance consistency. This study introduces RISEBench to address these gaps.

Limitations

The benchmark covers only English and Chinese, and the evaluation framework relies on human judges and an LMM-as-a-judge approach, which introduces uncertainty in judgment calibration.

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