Stride: Scale-Aware Multi-Agent Evolutionary Refinement for Infographic Generation

Key points

• Infographic generation challenges text-to-image models because a single image must satisfy numerous visual, textual, and semantic constraints.1
• Iterative agentic refinement with a multimodal verifier is a natural remedy, but existing methods face a scale mismatch: local edits cannot correct global composition errors, while full regeneration may destroy already-correct regions.1
• Stride separates constraint evaluation - whether each requirement is satisfied - from visual grounding - where and how it is violated.1
• With fully open-weight generator-verifier systems, Stride outperforms proprietary systems including Nano-Banana-2.0 and Seedream-5.0-Pro on BizGenEval, matches GPT-Image-2.0 and surpasses Seedream-5.0-Pro on IGenBench, and achieves a new state-of-the-art score on WISE, while substantially outperforming existing agentic methods across all evaluated benchmarks.1

Sources (1)

• [1]Stride: Scale-Aware Multi-Agent Evolutionary Refinement for Infographic Generation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 12:29 PM

  We introduce Stride, a constraint-grounded multi-agent framework that treats refinement over any frozen generator as a history-aware evolution strategy.

  Infographic generation challenges text-to-image models because a single image must satisfy numerous visual, textual, and semantic constraints.

Extractive summary: sentences quoted from the sources.