Innovation
Methods, ideas, or system contributions that make the work stand out.
Visible Chain-of-Thought
Analytics Code Generation
Persona-dependent Reasoning
Controlled Ablation Framework
Internal Reasoning Configuration
Visible Reasoning Is Not a Universal Optimizer: Persona- and Thinking-Dependent Effects in Analytics Code Generation
🤖 AI Summary
This study investigates whether visible chain-of-thought reasoning universally enhances analytical code generation, with particular attention to the hypothesis that its utility increases when the reasoning representation aligns with the target language. We propose a controlled execution-based comparison framework and conduct ablation studies on a query-matched SQL-pandas benchmark to systematically disentangle the independent effects of reasoning content and prompt formatting, while evaluating interactions among persona, target language, and reasoning configurations. Results demonstrate that visible reasoning is not a universally effective default strategy; rather, its benefits depend heavily on specific combinations of model architecture, role assignment, and task objectives. This work challenges the prevailing assumption regarding the universal efficacy of visible reasoning, delineates its actual utility boundaries, and provides empirical guidance for configuring targeted reasoning strategies in practical applications.
📝 Abstract
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.
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