Relative importance of general reasoning versus coding specialization for SciCoQA discrepancy detection

Determine whether general instruction-following and reasoning abilities provide greater benefit than specialized coding knowledge for detecting paper–code discrepancies in the SciCoQA dataset, as indicated by the observed result that GPT-5 Mini outperforms GPT-5 Codex despite the latter’s stronger code-generation capabilities.

Background

SciCoQA evaluates models on detecting discrepancies between scientific papers and their corresponding code repositories. In the results, the authors observe that GPT-5 Mini outperforms GPT-5 Codex, even though Codex is based on GPT-5 and is generally superior in code generation.

Based on this observation, the authors conjecture that general instruction-following and reasoning abilities may be more helpful than specialized coding knowledge for this task. Establishing whether this conjecture holds would clarify which capabilities most strongly drive performance on SciCoQA and guide future model development and evaluation.