arXiv AI By Bhawani Shankar Leelar, Pawan Chorasiya, Davin Hill, Robert E. Tillman, Tamer Soliman

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

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The paper investigates whether visible chain-of-thought (CoT) reasoning universally improves analytics code generation across different target languages (SQL vs. Python pandas). Using a benchmark that varies persona phrasing, target language, CoT format, and model settings, the authors find no consistent advantage from visible CoT or from matching the reasoning representation to the target language. Instead, the impact of visible reasoning depends on the persona, target, model configuration, and internal-reasoning setting, suggesting that reasoning strategies must be tailored rather than applied as a universal default.

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arXiv Machine Learning
Jun 16

Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpreter

arXiv:2606. 16934v1 Announce Type: cross Abstract: Reasoning with a Code Interpreter (CI) has emerged as an effective paradigm for enhancing the reasoning capabilities of large language models (LLMs) through executable computation and iterative verification.

By Patomporn Payoungkhamdee, Napat Laosaengpha, Jenta Wonglertsakul, Pittawat Taveekitworachai, Pume Tuchinda, Panjapong Poobanchuen, Ekapol Chuangsuwanich, Can Udomcharoenchaikit, Samuel Cahyawijaya, Peerat Limkonchotiwat, Sarana Nutanong