mlsanityData to 7 Oct 2026

Prompt Complexity Dilutes Structured Reasoning: A Follow-Up Study on the Car Wash Problem

arXiv 2603.13351 ↗First posted 7 Mar 2026cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 7 Mar 2026.

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References · 27

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  5. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  6. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  7. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
  8. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 778
  9. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  10. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 441
  11. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 299
  12. 2025Chain-of-Thought Reasoning In The Wild Is Not Always Faithfulcited by 166
15 more
  1. 2024The Impact of Reasoning Step Length on Large Language Modelscited by 152
  2. 2024Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Modelscited by 150
  3. 2024Premise Order Matters in Reasoning with Large Language Modelscited by 71
  4. 2024Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchycited by 62
  5. 2025IHEval: Evaluating Language Models on Following the Instruction Hierarchycited by 48
  6. 2025CRANE: Reasoning with constrained LLM generationcited by 38
  7. 2025Control Illusion: The Failure of Instruction Hierarchies in Large Language Modelscited by 31
  8. 2025Large Language Models and Mathematical Reasoning Failurescited by 22
  9. 2026Large Language Model Reasoning Failurescited by 20
  10. 2025Unable to Forget: Proactive Interference Reveals Working Memory Limits in LLMs Beyond Context Lengthcited by 18
  11. 2025How Many Instructions Can LLMs Follow at Once?cited by 17
  12. 2025Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoningcited by 8
  13. 2026ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Followingcited by 3
  14. 2026Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problemcited by 3
  15. 2026Deconstructing Instruction-Following: A New Benchmark for Granular Evaluation of Large Language Model Instruction Compliance Abilitiescited by 1

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