Mechanism-Guided Iteration for Execution-Grounded Game Design

Language-model agents can produce valid game specifications, yet they often solve each new design from scratch because execution experience is not converted into reusable causal knowledge. Consequently, repeated refinement may improve one artifact without accumulating design capability. We address this problem with mechanism-guided iteration, a frozen-backbone framework that consolidates successful executions into Component–Mechanism–Skill memory and uses failure-specific diagnostics to retrieve relevant rule fragments for local edits. Each proposal is compiled as a GameSpec and executed by a fixed interpreter. To separate genuine mechanism reuse from surface novelty or rule accumulation, a method-blind evaluator induces explanations from observed transitions and tests whether they transfer to changed initial states. We evaluate diagnosis and memory through equal-feedback controls under matched resource budgets on CreativeGame. The current ten-brief study is a development pilot, and confirmation on untouched briefs remains pending. The resulting protocol tests whether frozen agents can turn repeated correction into cumulative design learning.