• Computer Science > Artificial Intelligence [Submitted on 25 Feb 2026] Title:Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem View PDF HTML (experimental)Abstract:Large language models consistently fail the “car wash problem,” a viral reasoning benchmark requiring implicit physical constraint inference • We present a variable isolation study (n=20 per condition, 6 conditions, 120 total trials) examining which prompt architecture layers in a production system enable correct reasoning • 5 Sonnet with controlled hyperparameters (temperature 0 • 0), we find that the STAR (Situation-Task-Action-Result) reasoning framework alone raises accuracy from 0% to 85% (p=0 • 001, Fisher’s exact test, odds ratio 13 • Adding user profile context via vector database retrieval provides a further 10 percentage point gain, while RAG context contributes an additional 5 percentage points, achieving 100% accuracy in the full-stack condition

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  • Computer Science > Artificial Intelligence [Submitted on 25 Feb 2026] Title:Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem View PDF HTML (experimental)Abstract:Large language models consistently fail the “car wash problem,” a viral reasoning benchmark requiring implicit physical constraint inference. We present a variable isolation study (n=20 per condition, 6 conditions, 120 total trials) examining which prompt architecture layers in a production system enable correct reasoning. Using Claude 3.5 Sonnet with controlled hyperparameters (temp

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