EditAgent rethinks agentic image editing by expanding both the action and observation spaces. It routes each editing step to general editors and specialized methods, attaching verification records that allow off‑the‑shelf VLM judges to outperform finetuned reward models. The approach yields parallel candidate generation and a single listwise judgment, which experts prefer over sequential refinement in 78.5% of cases and improves two open‑weight editors across three benchmarks.
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.
By Jun He, Canmiao Fu, Ying Zhang, Zhipeng Huang, Chenyu Mu, Yazhen Xie, Chen Li, Jing Lyu, Weijia Li, Xiang Zhang
Stride is a multi‑agent framework for infographic generation that separates constraint evaluation from visual grounding, routing global revisions to a composer subagent and local corrections to a retoucher subagent. Each subagent plans internally and produces multiple candidates, and Stride accepts only those that improve a verifier score, thereby preventing regression across iterations. This scale‑aware, redundancy‑rich approach yields stable improvement and outperforms proprietary systems on several benchmarks, achieving state‑of‑the‑art results on WISE and strong performance on BizGenEval and IGenBench.
By Yan Zhou, Yang Yang
arXiv:2610.18097v1 Announce Type: cross
Abstract: Post-training is crucial for improving text-to-image models, yet existing pipelines typically rely on manually curated training data and fixed traini...
By Sirui Zhang, Fangyun Wei, Dong Chen, Jinjing Zhao, Yitong Wang, Yunuo Chen, Baining Guo, Zheng-Jun Zha
Self-Evolving Multimedia Verification through Memory Consolidation of Contestation Experiences (SEMV) is a multi-agent framework that uses provenance-bearing arguments to link evidence, reasoning, human contestation, and memory. It integrates arena-based quantitative bipolar argumentation, causal and scoped revision, and verification-gated memory consolidation with explicit conflict retention. On the COSMOS benchmark, SEMV achieves 91.88% accuracy, reducing negative transfer from 5.7% to 0.2%, and on the CTR benchmark it corrects 96.7% of initial errors while saving 52.8% of compute.
By Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Hoang-Loc Cao, Phuc Ho, Truong Thinh Nguyen, Van Pham, Hung Cao
WorldFact-Bench is a new benchmark that tests image‑world consistency by presenting 1,274 real‑fake image pairs across four verification regimes and ten semantic domains, each pair containing a factual conflict while keeping the image visually plausible. The evaluation requires both images in a pair to be classified correctly, and the benchmark focuses on single‑image assessment without predefined claims. The authors also introduce PERSIST‑Agent, an iterative verification system that maintains a persistent state linking candidate facts, visual observations, evidence, and verification statuses, and shows improved pair accuracy over baseline methods on 8B backbones.
By Zhuohong Chen, Zhengxian Wu, Yunyao Yu, Hangrui Xu, Zijian Yu, Hao Tan, Zhifang Liu, Peng Jiao, Jun Lan, Haoqian Wang