Abstract
Fine-tuned code LLMs are often conditioned on a design-intent header to steer parametric CAD generation, but whether the model reads that header's content has been tested neither under execution-level scoring nor with a causal control. We study CADCON, a five-feature design-intent header prepended to CadQuery-style sketch-extrude programs during LoRA fine-tuning of Qwen2.5-Coder-1.5B, re-scored by executable geometric assertions on the produced B-rep solid. Across three seeds and a pre-registered {0%, 40%}-prefix {correct, wrong, masked}-header matrix -- with inference duplicate-aware over the 38 unique-program clusters a submission-stage audit found held out -- we report: (i) in conditional completion (40% prefix), a semantically wrong header degrades adherence below the no-header baseline (0.43 0.30/0.21 text/token) on the intents the model can render unconditioned, namely polygonal and thin. The drop is significant on 3/3 token-header seeds but 1/3 text-header seeds, so the pre-registered combined rule, which required the text side, does not pass; (ii) a derangement control -- retrained with shuffled headers: identical marginal, greatly reduced content correlation -- stays competent yet shows no detectable correct-to-wrong drop while M does (text headers; interaction significant on 3/3 seeds, p 0.024, one seed below the frozen non-zero-count guardrail), so the harm requires the learned headerprogram mapping, not the header marginal; (iii) requiring a generated program to execute removes almost all of a correct header's apparent benefit (token: +0.22 ungated regex +0.03 gated regex +0.02 gated geometry), so the deflation is execution blindness, not detector disagreement; (iv) the harm is regime-specific: at 0% prefix the baseline generates no valid CAD at all. Wrong intent is not noise: where it is detectable, it actively misdirects generation.