UnicEdit-10M: A Dataset and Benchmark Breaking the Scale-Quality Barrier via Unified Verification for Reasoning-Enriched Edits

• URL: http://arxiv.org/abs/2512.02790v1
• Date: Mon, 01 Dec 2025 17:45:44 GMT
• Title: UnicEdit-10M: A Dataset and Benchmark Breaking the Scale-Quality Barrier via Unified Verification for Reasoning-Enriched Edits
• Abstract summary: We introduce a lightweight data pipeline that replaces multi-toolchains with an end-to-end model and a unified post-verification stage.<n>For scalable quality control, we train a 7B dual-task expert model, textbfQwen-Verify, for efficient failure detection and instruction recaptioning.<n>This pipeline yields textbfUnicEdit-10M, a 10M-scale dataset spanning diverse basic and complex editing tasks.
• Score: 43.59555184340113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advances of powerful multimodal models such as GPT-4o, Nano Banana, and Seedream 4.0 in Image Editing, the performance gap between closed-source and open-source models is widening, primarily due to the scarcity of large-scale, high-quality training data and comprehensive benchmarks capable of diagnosing model weaknesses across diverse editing behaviors. Existing data construction methods face a scale-quality trade-off: human annotations are high-quality but not scalable, while automated pipelines suffer from error propagation and noise. To address this, we introduce a lightweight data pipeline that replaces multi-toolchains with an end-to-end model and a unified post-verification stage. For scalable quality control, we train a 7B dual-task expert model, \textbf{Qwen-Verify}, for efficient failure detection and instruction recaptioning. This pipeline yields \textbf{UnicEdit-10M}, a 10M-scale dataset spanning diverse basic and complex editing tasks. We also propose \textbf{UnicBench}, a general benchmark that extends beyond basic edits to explicitly assess spatial and knowledge-driven reasoning. To enable fine-grained diagnosis, we introduce novel metrics, including \textit{Non-edit Consistency} and \textit{Reasoning Accuracy}. Our analysis of mainstream models on UnicBench reveals their limitations and provides clear directions for future research.

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