mlsanityData to 7 Oct 2026

Visible Reasoning Is Not a Universal Optimizer: Persona- and Thinking-Dependent Effects in Analytics Code Generation

arXiv 2610.10639 ↗First posted 7 Oct 2026cs.LG cs.AI cs.SEcited by 0

On the map: LLM reasoning → Prompting and chain-of-thought reasoning also fits: Reasoning benchmarks and evaluation

Abstract (arXiv)

Visible Chain-of-Thought (CoT) is often treated as a broadly useful reasoning instruction, yet analytics code generation combines natural-language ambiguity, schema grounding, target-language constraints, and model-specific inference behavior. Because the same analytics request can be expressed in two distinct target languages-SQL and Python (pandas)-this setting provides a natural test of a common but under-examined assumption: that visible reasoning is more effective when its representation matches the requested target, as in "think in SQL" or "think in Python." Together with generic instructions such as "think step-by-step," such recommendations remain insufficiently evaluated under controlled, execution-based comparisons. We study a query matched SQL-pandas benchmark that crosses persona phrasing, target language, visible-CoT format, control prefixes, direct generation, and internal-reasoning configurations. The results do not support either a universal accuracy advantage from visible CoT or a consistent benefit from matching the reasoning representation to the target language. Instead, the effects depend on the persona, target, model configuration, and internal-reasoning setting. The control ablations further distinguish effects of reasoning content from those of prompt format. These findings indicate that reasoning strategies should be selected jointly for the model, persona, target, and internal-reasoning configuration rather than adopted as universal defaults. More broadly, the study provides a controlled framework for identifying when visible reasoning improves executable generation, when it primarily perturbs model behavior, and when the internal-reasoning configuration is the more consequential factor.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 8 of its 31 references that are in the map. First posted on arXiv 7 Oct 2026.

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8 of 31 references.

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  1. 2023Measuring Faithfulness in Chain-of-Thought Reasoning17 Jul

Other references · 7

  1. 2024Quiet-STaR: Language Models Can Teach Themselves to Think Before Speakingcited by 292
  2. 2025Uncertainty-Guided Chain-of-Thought for Code Generation with LLMscited by 30
  3. 2025DSCodeBench: A Realistic Benchmark for Data Science Code Generationcited by 15
  4. 2026Broken Chains: The Cost of Incomplete Reasoning in LLMscited by 4
  5. 2025Understanding Chain-of-Thought Effectiveness in Code Generation: An Empirical and Information-Theoretic Analysiscited by 2
  6. 2024Assessing Code Generation with Intermediate Languagescited by 1
  7. 2026Benchmarking Text-to-Python against Text-to-SQL: The Impact of Explicit Logic and Ambiguitycited by 1

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  3. 2026CodeEval: A pedagogical approach for targeted evaluation of code-trained Large Language Models
  4. 2026Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpreter
  5. 2026Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents
  6. 2025SEER: Enhancing Chain-of-Thought Code Generation through Self-Exploring Deep Reasoning
  7. 2025Which Data Attributes Stimulate Math and Code Reasoning? An Investigation via Influence Functions
  8. 2026Think Anywhere in Code Generation