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Visible Reasoning Is Not a Universal Optimizer: Persona- and Thinking-Dependent Effects in Analytics Code Generation — arXiv:2610.10639

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10639
License (as archived): CC0-1.0
Archived: 2026-10-09T05:53:51.255Z
SHA-256 of the archived text: e5e387b515aa29ac71a28ada19610eddac00e134cc9fbfd2f9ffdf20ca6473a7
Stable URL of this page: https://metraipolis.com/documento/dc_adbd355fd149

How to cite

Visible Reasoning Is Not a Universal Optimizer: Persona- and Thinking-Dependent Effects in Analytics Code Generation — arXiv:2610.10639 — arXiv (en), https://arxiv.org/abs/2610.10639 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_adbd355fd149 (Metraipolis archive dc_adbd355fd149, sha256 e5e387b515aa29ac71a28ada19610eddac00e134cc9fbfd2f9ffdf20ca6473a7)

BibTeX

@misc{dcadbd355fd149,
  title = {Visible Reasoning Is Not a Universal Optimizer: Persona- and Thinking-Dependent Effects in Analytics Code Generation — arXiv:2610.10639},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10639},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_adbd355fd149; sha256 e5e387b515aa29ac71a28ada19610eddac00e134cc9fbfd2f9ffdf20ca6473a7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10639. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2610.10639 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T05:47:01.289Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).