让AI推理过程可编辑，用户能参与思考并调整结果。
Co-CoT: A Prompt-Based Framework for Collaborative Chain-of-Thought Reasoning
- 将AI推理拆成可查看、修改、重跑的模块化步骤。
- 支持用户根据自身思维习惯调整推理路径。
- 适合关注AI透明性与人机协作的研究者和开发者。
随着短视频内容泛滥和AI普及，深度反思性思考的机会显著减少，削弱了用户批判性思维能力，并降低了对AI生成结果推理过程的理解。为解决此问题，我们提出一种交互式思维链（Interactive Chain-of-Thought, CoT）框架，通过使模型推理过程透明、模块化且可由用户编辑，增强以人为本的可解释性与负责任的AI使用。该框架将推理分解为清晰定义的逻辑块，允许用户检查、修改并重新执行，促进主动认知参与而非被动接受。其进一步整合了一种轻量级编辑自适应机制，灵感来自偏好学习，使系统能够适配多样化的认知风格与用户意图。伦理透明性通过显式元数据披露、内置偏见检查点功能以及隐私保护机制得以保障。本研究阐明了在应对复杂社会挑战的AI系统中，推动批判性参与、负责任交互与包容性适配所需的设计原则与架构。
原文摘要 · Abstract (English)
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.
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