Answer Extraction Changes Conclusions in Controlled Mathematical Reasoning Tests

A mathematical answer score combines the response a model produces with the rule used to read that response. We separate these two components in a controlled study of six mathematical template families. The dataset contains 240 programmatically checked problems, each presented with and without a task-specific reasoning cue. Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct produce 960 responses under a common decoding protocol. A strict final-answer rule assigns correct scores to 217 of 480 Qwen responses and only 2 of 480 Llama responses. Yet the rule fails to extract an answer from 477 Llama responses, including texts that state a numerical conclusion in ordinary language. A post hoc, label-blind terminal-answer sensitivity recovers 242 additional Llama predictions, raising its correct count to 135 without changing any generated text. Under this sensitivity, the mean cue effect is $-0.4$ percentage points for Qwen and $-2.9$ points for Llama, with descriptive family-level intervals spanning zero. Individual family effects vary substantially. These results do not establish either a universal cue benefit or a model ranking in mathematical reasoning. They show why a benchmark must distinguish answer-format compliance, extractable correctness, and the effect of a reasoning intervention. We release paired prompts, exact labels, complete responses, and both scoring rules so that these quantities can be recomputed separately.