arXiv:2607. 05465v1 Announce Type: cross Abstract: Complex image creation and editing often require more than a single generation or editing model.
Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation.
arXiv:2606. 01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction following, minimally editing the source image, and ensuring high visual quality.
arXiv:2509. 24900v2 Announce Type: replace-cross Abstract: The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data.
WeAgent-MMGenEdit is a comprehensive framework for multimodal agentic image generation and editing that addresses the unreliability of current models when prompts require external world knowledge. It introduces a multimodal harness with persistent evidence management, a scalable data construction pipeline producing 23K supervised trajectories and 14.7K RL tasks, and a bilingual benchmark (WeBench-MMGenEdit) for knowledge-intensive generation and multi-image editing. Post‑training methods based on SFT and RL further refine the agent policy and image backend, enabling a 30B‑parameter policy to outperform similarly sized models and approach the performance of a 1T‑parameter agent.
SenseNova-U1.5 is an 8B‑MoT native unified multimodal model that can understand, reason about, and generate visual content without using an encoder or VAE. It improves visual fidelity and text rendering through spatially coherent patch reconstruction, large‑scale training on curated generation and editing data, and native resolutions up to 4K. Post‑training, specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing are optimized and distilled into a multi‑expert framework, yielding advances in image fidelity, complex composition, multi‑reference editing, and instruction following.
The paper introduces WeGenEditBench, a benchmark with 2,740 evaluation instances and 148 task types for image editing, focusing on local failure detection and prioritization for model improvement. It also presents WeGenEvoStudio, a multi‑agent harness that supports recursive self‑improvement by diagnosing failures, constructing retrieval‑grounded data, and adapting models. Experiments show that models such as GPT‑Image‑2 and Seedream‑5.0‑Pro excel in single‑image and text‑intensive editing, and evolution experiments demonstrate significant targeted gains on selected benchmarks.
EditJev is a System One routing model that selects the best image editing model from a candidate pool for a given image and instruction. It predicts sample-level preference scores in a single forward pass using a lightweight head on a vision‑language backbone, avoiding autoregressive generation or parsing. By combining these scores with model speed metrics, EditJev improves overall editing quality while cutting end‑to‑end latency by up to 60.8%.
arXiv:2607. 16409v1 Announce Type: cross Abstract: Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation.
MTEdit is a new multi‑turn image editing dataset that uses packed trajectory canvas (PTC) generation to create 500K editing trajectories, each up to eight turns with an average of 5.38 turns. The dataset addresses visual‑quality drift by jointly generating multiple editing states within a unified visual context, covering both step‑wise and context‑aware editing scenarios. MTEdit-Bench evaluates models on instruction following, content consistency, visual‑quality drift resistance, and historical context usage, and fine‑tuned Qwen‑Image‑MTEdit outperforms other open‑source models on this benchmark.
GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step workflows. However, the diversity of available tr...
MoI-Bench is a new benchmark designed for on‑device image generation and editing, featuring 64 subtasks and 1150 samples that cover 16 operations and eight visual target categories. It introduces MoI‑Eval, a task‑specific evaluation framework that assesses instruction following, visual quality, and detail preservation, and aggregates results into a two‑dimensional Quality‑Deployability map. The benchmark reveals that current on‑device models excel at efficient generation or editing but need improved editing quality and broader task coverage, especially for text replacement and identity preservation.