Code-Guided Reasoning for Small Language Models: Evaluating Executable MCQA Scaffolds
Multiple-choice QA benchmarks usually evaluate small language models (SLMs) as direct answerers, but deployed language-model systems increasingly rely on external scaffolds such as tools, code, and repeated model calls. We introduce Code-Guided Reasoning (CGR), an evaluation protocol and generated-program resource for measuring when executable reasoning scaffolds improve SLM performance on MCQA tasks. CGR standardizes six components: a normalized item interface, a direct solver prompt, a generator prompt, a Python scaffold, solver-call and extraction helpers, and a three-channel result record. On 20,498 retained result rows from a locally prepared MCQA bundle and six metadata-registered s...
| Authors | Prateek Biswas, Dhaval Patel, Vedant Khandelwal et al. |
|---|---|
| Primary category | cs.IR |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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