Problem & motivation
Turning heterogeneous paper material into a usable, condensed artifact requires evidence selection, planning, and iterative repair over a long horizon.
Look for the paper-to-artifact path in Figure 6.
AutoDesign · research companion
Meta-Harness Optimization for Long-Horizon Agentic Design
AutoDesign treats human-aligned design generation as an opportunity to improve the harness around a fixed model.
Across rollout feedback, the learned DesignHarness turns paper evidence into editable, source-grounded artifacts and evaluates the result with PosterBench.
Method
Results
A trace of harness improvement, then a reusable gain across configurations.
The paper tracks autonomous improvement, a plateau, and a further gain after human guidance redirects the search; the learned DesignHarness improves all seven tested configurations.
At a glance
The harness, not the model, is what learns.
The paper holds model parameters fixed and improves the surrounding design harness from execution traces and evaluation evidence.
Approach
An inner loop makes and revises an artifact; an outer loop analyzes rollouts, proposes one component update, and gates promotion on evaluation evidence.
Trace the nested loops in Figure 3.
Reported takeaways
AutoDesign reports a 78.32 main-track score; attaching DesignHarness raises the reported mean across seven controlled configurations from 54.99 to 67.39.
Inspect the trace and comparison in Figure 1.
AutoDesign designs the poster for AutoDesign.
Reading cue: one long-horizon workflow performs source ingestion, iterative generation and refinement, critic feedback integration, and finalization in about 40 minutes.
Method
One loop refines artifacts. Another refines the harness.
Follow a step to see its local role. The figures below keep the pipeline tied to the paper’s own system diagrams.
One outer-loop iteration, made legible.
Reading cue: rollout, evaluation, update proposal, and acceptance repeat autonomously; human guidance redirects evaluation and optimization when needed.
The inner loop refines artifacts; the outer loop refines the harness.
Reading cue: critic feedback revises the current artifact, while rollout evidence determines whether one harness component is accepted or rejected.