AutoDesign能自动把论文变海报，PosterBench上比Claude Design高7.45分，40分钟成本不到3美元。
AutoDesign是面向长周期智能体设计的框架，能将多模态源转化为结构化媒体输出，聚焦论文到海报生成任务。该框架引入PosterBench基准，主轨含100篇论文，横跨五个学科，验证子集含10篇。在主轨上AutoDesign得分78.32，超过闭源系统Claude Design 7.45分。在七个受控配置中，加入DesignHarness将平均得分由54.99提至67.39，提升12.4%。完全自主循环中它执行253次工具调用和11轮编辑，40分钟内成本低于3美元。
AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design
Transforming multimodal sources into condensed and structured media outputs can be fundamentally conceptualized as a long-horizon agentic process centered on a model-harness system. While an ideal harness system should align with human design priors and accumulate reusable experience through empirical exploration to drive recursive self-improvement, existing paradigms remain static and fall short of this capability. In this paper, we present AutoDesign, a framework that aligns with human design priors, where a meta-harness optimizer guides a code agent to recursively improve harness based on rollout feedback. To instantiate and evaluate this framework, we focus on the academic paper-to-poster generation task and introduce PosterBench, comprising a 100-paper Main Track spanning five disciplines and PosterBench-mini, a shared 10-paper subset for controlled evaluation. On the PosterBench Main Track, AutoDesign achieves the highest score of 78.32, 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.