AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design
Score
76提出新的元优化框架和PosterBench基准，对长周期智能体自改进方向有显著增量贡献。
方法设计完整，实验对比充分，包含多种配置和人工评测，论证严谨。
展示了低成本、高效率的自动化内容生成方案，对智能体系统落地有直接参考价值。
当前智能体自改进与自动化内容生产是热点方向，论文发布及时且前沿。
来源为arXiv预印本，尚未经过同行评审，但作者团队与基准设计呈现出较高专业水准。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper presents a generalizable meta-optimization framework with a large-scale benchmark, delivering convincing results and strong implications for agent research and applications.
AI commentary
「This paper stands out for turning 'agent self-improvement' from a slogan into a quantifiable, verifiable framework, and generating high-quality posters for under $3 is especially attractive for resource-constrained teams. For those building agent products, automated design tools, or content pipelines, this methodology is likely directly adaptable.」
Quick overview
AutoDesign presents a meta-optimization framework that leverages a code agent to recursively improve the model-harness system based on rollout feedback, enabling self-improving long-horizon agentic design. The paper focuses on academic paper-to-poster generation and introduces PosterBench, a benchmark comprising 100 papers across five disciplines. AutoDesign achieves a score of 78.32 on PosterBench Main Track, surpassing the closed-source commercial system Claude Design by 7.45 points. Across seven controlled code-agent-model configurations, integrating the learned DesignHarness consistently improves performance, increasing the average PosterBench Score from 54.99 to 67.39 (+12.4%). In a fully autonomous long-horizon loop, it executes 253 tool calls and 11 editing turns within 40 minutes for under $3, reaching average conference-poster quality in human evaluation. A system-blind human study further demonstrates that AutoDesign achieves the highest human preference among evaluated systems.
- Proposes AutoDesign, a meta-optimization framework where a code agent recursively improves the model-harness system based on rollout feedback for long-horizon self-improvement.
- Introduces PosterBench, a 100-paper Main Track spanning five disciplines plus a shared 10-paper mini subset for controlled evaluation.
- AutoDesign scores 78.32 on PosterBench Main Track, surpassing Claude Design by 7.45 points; the learned DesignHarness improves average score from 54.99 to 67.39 (+12.4%) across seven configurations.
- In a fully autonomous loop, it executes 253 tool calls and 11 editing turns within 40 minutes under $3, reaching average conference-poster quality in human evaluation.
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