DepthBenchCAD: When Does Deeper Auditing Yield More Reliable Conclusions?
Generative CAD models are expected to remain behaviorally correct after parameter edits, so increasing the number of edit checks is often treated as a direct route to more reliable evaluation. Under a fixed budget, however, auditing each program more thoroughly reduces the number of tasks and independent generations that can be evaluated, which can ultimately make model-level estimates less accurate. We study this phenomenon and the conditions under which it arises. We decompose behavioral evaluation into three evidence levels: task templates, stochastic generations, and within-program edits. We define an average failure risk that is invariant to audit depth, and combine three-level varia...
| Authors | Hongye Yang, Zhihao Xie, Shengjun Xiong et al. |
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| Primary category | cs.SE |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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