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
• Authors: Keming Ye, Zhipeng Huang, Canmiao Fu, Qingyang Liu, Jiani Cai, Zheqi Lv, Chen Li, Jing Lyu, Zhou Zhao, Shengyu Zhang,
• 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.

Related papers

• Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition [12.731093427395985]
  ADAMAB is an efficient embedding calibration framework for few-shot pattern recognition.<n>Our experiments justify the superior performance of ADAMAB, with up to 40% accuracy improvement.
  arXiv  Detail & Related papers  (2026-02-22T23:39:21Z)
• FarSkip-Collective: Unhobbling Blocking Communication in Mixture of Experts Models [17.64873155970997]
  We present FarSkip-Collective which modifies the architecture of modern models to enable overlapping of their computation with communication.<n>We fully convert a series of state-of-the-art models varying from 16B to 109B parameters to enable overlapping of their communication.<n>In addition to demonstrating retained accuracy of the large modified models, we realize the benefits of FarSkip-Collective through optimized implementations.
  arXiv  Detail & Related papers  (2025-11-14T17:25:14Z)
• ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset [43.45582911794623]
  We introduce ToolMind, a high-quality tool-agentic dataset with 160k synthetic data instances.<n>We employ fine-grained turn-level filtering to remove erroneous or suboptimal steps.<n>Models fine-tuned on ToolMind show significant improvements over baselines on several benchmarks.
  arXiv  Detail & Related papers  (2025-11-12T13:01:23Z)
• SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge [7.655956608192742]
  We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large Language Model (LLM) short-form factuality based on OpenAI's SimpleQA.<n>It addresses critical limitations in OpenAI's benchmark, including noisy and incorrect labels, topical biases, and question redundancy.<n>On this new benchmark, Gemini 2.5 Pro achieves a state-of-the-art F1-score of 55.6, outperforming other frontier models.
  arXiv  Detail & Related papers  (2025-09-09T17:53:58Z)
• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)
• Whoever Started the Interference Should End It: Guiding Data-Free Model Merging via Task Vectors [27.848233831749216]
  textbfWUDI-Merging (textbfWhoever started the interference shotextbfUld entextbfD textbfIt) is a model merging method that eliminates interference without any additional data or rescaling coefficients.<n> Comprehensive empirical evaluations across vision and language benchmarks demonstrate our method's superiority.
  arXiv  Detail & Related papers  (2025-03-11T07:01:35Z)
• Mask Factory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation [70.95380821618711]
  Dichotomous Image (DIS) tasks require highly precise annotations.<n>Current generative models and techniques struggle with the issues of scene deviations, noise-induced errors, and limited training sample variability.<n>We introduce a novel approach, which provides a scalable solution for generating diverse and precise datasets.
  arXiv  Detail & Related papers  (2024-12-26T06:37:25Z)
• NeKo: Toward Post Recognition Generative Correction Large Language Models with Task-Oriented Experts [57.53692236201343]
  We propose a Multi-Task Correction MoE, where we train the experts to become an expert'' of speech-to-text, language-to-text and vision-to-text datasets. NeKo performs competitively on grammar and post-OCR correction as a multi-task model.
  arXiv  Detail & Related papers  (2024-11-08T20:11:24Z)
• G-SPEED: General SParse Efficient Editing MoDel [25.48360227520061]
  underlinetextbfGeneral underlinetextbfSParse underlinetextbfEfficient underlinetextbfEditing MounderlinetextbfDel(textbfG-SPEED)
  arXiv  Detail & Related papers  (2023-10-16T15:01:18Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• Bridging the Gap Between Clean Data Training and Real-World Inference for Spoken Language Understanding [76.89426311082927]
  Existing models are trained on clean data, which causes a textitgap between clean data training and real-world inference. We propose a method from the perspective of domain adaptation, by which both high- and low-quality samples are embedding into similar vector space. Experiments on the widely-used dataset, Snips, and large scale in-house dataset (10 million training examples) demonstrate that this method not only outperforms the baseline models on real-world (noisy) corpus but also enhances the robustness, that is, it produces high-quality results under a noisy environment.
  arXiv  Detail & Related papers  (2021-04-13T17:54:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.