Artifact Memory: Guiding Agentic Artifact Editing with Edit Graphs and Conflict Retrieval

Long-horizon artifact editing requires agents to manage interactions among components and dependencies across editing decisions. Many edit consequences become apparent only after rendering, yet retaining every intermediate version and observation is costly and does not explain recurring repair failures. We propose Artifact Memory to address both the representational and algorithmic needs of artifact editing. Its edit graph shares unchanged components across recoverable versions and links edits to component-level validation outcomes. Relevance-based retrieval selects prior attempts and partial successes, while conflict analysis connects outcomes across versions to identify repairs that undo earlier progress. This context supports coordinated repairs, backtracking, and alternative editing sequences while retaining lessons from abandoned branches. Across HTML/CSS poster editing, SVG editing, and Blender scene editing with Claude Opus 4.8 and GPT-5.6 Terra, Artifact Memory improves constraint-satisfaction success rates over validator-feedback baselines by 10.0–31.8 percentage points. and reduces mean inference cost by 26–39% on tasks solved by both methods. Further trajectory analyses show lower capped repair-cycle recovery costs across domains, more productive backtracking and less repetition of failed continuations.