Signal AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design

Summary

The paper frames turning multimodal sources into condensed, structured media outputs as a long horizon agentic process centered on a model harness system. It argues that an ideal harness should align with human design priors and accumulate reusable experience through empirical exploration to drive recursive self improvement. It contends that existing paradigms remain static and fall short of that goal. The proposed AutoDesign approach responds by optimizing the meta harness, the scaffolding and toolchain surrounding the model, rather than the model itself. The aim is to improve performance on long horizon agentic design tasks, and the abstract presents this as a purely AI agent architecture contribution without explicit policy or societal impact framing.