Design Anything keeps design intent across long agent workflows
Original title: Design Anything, Design at Scale
With the model held fixed, it met 85.0% and 92.5% of factual constraints for DeepSeek V4.1 Flash High and GPT-5.6 Sol High.
Design Anything is a graph-based harness that stores design intent outside the model and binds deliverables to the workflow steps that produce them. A task contract keeps constraints and decisions, selected designs become inputs to later generations, and a deterministic controller checks output types and counts. DesignAnything-Bench has 228 tasks across 19 design domains, including 38 project families at 6, 14, and 24 artifacts. With the LLM fixed, it beat the strongest same-model baselines by 15.4% and 14.4% in constraint satisfaction, and designers preferred its complete sets in a blind evaluation.
- Gains were 25.1% and 23.0% on 24-artifact tasks.
- It led or tied constraint satisfaction in 18 of 19 domains under each model.
- The bench includes 38 project families at scales of 6, 14, and 24 artifacts.
- Professional designers preferred its complete deliverable sets in a blind evaluation.
From the original article
1 min read at huggingface.co
As multimodal resources are transformed into media outputs, agentic AI is expected to stay aligned with design intent over long horizons. Designers follow briefs specifying goals, constraints, and deliverables, with later work building on earlier decisions. Recent work reuses design experience across tasks, yet alignment among artifacts within one project remains challenging.
By Xuchao Ren, Siyuan Feng, Jing Huang · huggingface.co
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