Co-CoT：一种基于提示的协作式思维链推理框架
Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning
AI总结：
针对短视频和AI普及削弱深度思考的问题，提出交互式思维链框架，通过模块化、可编辑的推理过程增强可解释性，并整合偏好学习机制，促进用户批判性参与和负责任AI使用。
摘要：
由于短视频内容的激增和人工智能的快速普及，深度反思性思考的机会显著减少，这削弱了用户的批判性思维，并降低了他们对人工智能生成输出背后推理过程的参与度。为解决这一问题，我们提出了一种交互式思维链（CoT）框架，该框架通过使模型的推理过程透明化、模块化和用户可编辑，增强了以人为中心的可解释性和负责任的人工智能使用。该框架将推理分解为清晰定义的模块，用户可以检查、修改和重新执行这些模块，从而鼓励主动的认知参与而非被动消费。它进一步整合了一种受偏好学习启发的轻量级编辑适应机制，使系统能够适应多样化的认知风格和用户意图。通过明确的元数据披露、内置的偏见检查功能以及隐私保护措施，确保了伦理透明度。这项工作概述了促进批判性参与、负责任交互和包容性适应的设计原则和架构，旨在应对复杂的社会挑战。
英文摘要：
Due to the proliferation of short-form content and the rapid adoption of AI, opportunities for deep, reflective thinking have significantly diminished, undermining users' critical thinking and reducing engagement with the reasoning behind AI-generated outputs. To address this issue, we propose an Interactive Chain-of-Thought (CoT) Framework that enhances human-centered explainability and responsible AI usage by making the model's inference process transparent, modular, and user-editable. The framework decomposes reasoning into clearly defined blocks that users can inspect, modify, and re-execute, encouraging active cognitive engagement rather than passive consumption. It further integrates a lightweight edit-adaptation mechanism inspired by preference learning, allowing the system to align with diverse cognitive styles and user intentions. Ethical transparency is ensured through explicit metadata disclosure, built-in bias checkpoint functionality, and privacy-preserving safeguards. This work outlines the design principles and architecture necessary to promote critical engagement, responsible interaction, and inclusive adaptation in AI systems aimed at addressing complex societal challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
