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arXiv · 2503.14521

Policy Frameworks for Transparent Chain-of-Thought Reasoning in Large Language Models

Yihang Chen·Haikang Deng·Kaiqiao Han·Qingyue Zhao
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Abstract

Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by decomposing complex problems into step-by-step solutions, improving performance on reasoning tasks. However, current CoT disclosure policies vary widely across different models in frontend visibility, API access, and pricing strategies, lacking a unified policy framework. This paper analyzes the dual-edged implications of full CoT disclosure: while it empowers small-model distillation, fosters trust, and enables error diagnosis, it also risks violating intellectual property, enabling misuse, and incurring operational costs. We propose a tiered-access policy framework that balances transparency, accountability, and security by tailoring CoT availability to academic, business, and general users through ethical licensing, structured reasoning outputs, and cross-tier safeguards. By harmonizing accessibility with ethical and operational considerations, this framework aims to advance responsible AI deployment while mitigating risks of misuse or misinterpretation.

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Yihang Chen, Haikang Deng, Kaiqiao Han, Qingyue Zhao. 2025-03-14. Policy Frameworks for Transparent Chain-of-Thought Reasoning in Large Language Models. https://arxiv.org/abs/2503.14521

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