arXiv:2603.13351v1 Announce Type: new
Abstract: In a previous study [Jo, 2026], STAR reasoning (Situation, Task, Action, Result) raised car wash problem accuracy from 0% to 85% on Claude Sonnet 4.5, and to 100% with
additional prompt layers. This follow-up asks: does STAR maintain its effectiveness in a production system prompt?
We tested STAR inside InterviewMate’s 60+ line production prompt, which had evolved through iterative additions of style guidelines, format instructions, and profile
features. Three conditions, 20 trials each, on Claude Sonnet 4.6: (A) production prompt with Anthropic profile, (B) production prompt with default profile, (C) original
STAR-only prompt. C scored 100% (verified at n=100). A and B scored 0% and 30%.
Prompt complexity dilutes structured reasoning. STAR achieves 100% in isolation but degrades to 0-30% when surrounded by competing instructions. The mechanism: directives
like “Lead with specifics” force conclusion-first output, reversing the reason-then-conclude order that makes STAR effective. In one case, the model output “Short answer:
Walk.” then executed STAR reasoning that correctly identified the constraint — proving the model could reason correctly but had already committed to the wrong answer.
Cross-model comparison shows STAR-only improved from 85% (Sonnet 4.5) to 100% (Sonnet 4.6) without prompt changes, suggesting model upgrades amplify structured reasoning
in isolation.
These results imply structured reasoning frameworks should not be assumed to transfer from isolated testing to complex prompt environments. The order in which a model
reasons and concludes is a first-class design variable.
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