This paper examines what happens when GenAI tools are fully embedded in the drafting of an academic paper rather than confined to late-stage polishing. To investigate how an intensive multi-tool GenAI workflow differs from conventional academic writing, I drafted this paper from the first sentence in parallel with three GenAI tools - Claude, ChatGPT, and Gemini - comparing their outputs against my own intended contribution. Across this process, a recurring pattern took shape that I call adversarial co-thinking: using past peer reviews to calibrate the tools, then setting their outputs against one another to be tested rather than deferred to. I argue that surfacing genuine critique from tools that default to praise is a central practical challenge of working with these tools, and that the skill at stake is evaluative rather than generative. Adversarial co-thinking is a high-skill epistemic practice: it can amplify expertise where it exists, but it can also mask its absence. I further argue that current disclosure frameworks are poorly equipped to capture this shift. The paper offers four propositions for workshop discussion concerning autonomy, supervision, equity of access, and disclosure.

翻译：本文探讨当生成式人工智能工具完全嵌入学术论文的初稿撰写阶段，而非局限于后期润色环节时会发生什么。为探究高强度的多工具生成式人工智能工作流与传统学术写作的差异，笔者从论文首句开始，同步使用Claude、ChatGPT和Gemini三种生成式人工智能工具进行草拟，并将其输出与自身预期贡献进行比较。在这一过程中，一种反复出现的模式逐渐成形——笔者称之为"对抗共思"：利用过往同行评审意见校准工具，然后让各工具的输出相互竞争以供检验，而非盲目依从。本文认为，从默认倾向于赞美的工具中激发出真实批评，是使用这些工具面临的核心实践挑战，而其中关键技能在于评估而非生成。"对抗共思"是一种高水平的认知实践：它能在现有专业知识基础上放大专业能力，但也可能掩盖专业知识的缺失。本文进一步指出，当前披露框架难以充分捕捉这一转变。本文提出四项供工作坊讨论的命题，涉及自主性、监督、获取平等性及披露机制。