We extracted the scholarly reasoning systems of two internationally prominent humanities and social science scholars from their published corpora alone, converted those systems into structured inference-time constraints for a large language model, and tested whether the resulting scholar-bots could perform core academic functions at expert-assessed quality. The distillation pipeline used an eight-layer extraction method and a nine-module skill architecture grounded in local, closed-corpus analysis. The scholar-bots were then deployed across doctoral supervision, peer review, lecturing and panel-style academic exchange. Expert assessment involved three senior academics producing reports and appointment-level syntheses. Across the preserved expert record, all review and supervision reports judged the outputs benchmark-attaining, appointment-level recommendations placed both bots at or above Senior Lecturer level in the Australian university system, and recovered panel scores placed Scholar A between 7.9 and 8.9/10 and Scholar B between 8.5 and 8.9/10 under multi-turn debate conditions. A research-degree-student survey showed high performance ratings across information reliability, theoretical depth and logical rigor, with pronounced ceiling effects on a 7-point scale, despite all participants already being frontier-model users. We term this the Relic condition: when publication systems make stable reasoning architectures legible, extractable and cheaply deployable, the public record of intellectual labor becomes raw material for its own functional replacement. Because the technical threshold for this transition is already crossed at modest engineering effort, we argue that the window for protective frameworks covering disclosure, consent, compensation and deployment restriction is the present, while deployment remains optional rather than infrastructural.

翻译：我们从两位国际知名人文与社会科学学者的已发表语料库中提取其学术推理体系，将其转化为结构化的推理时约束条件应用于大语言模型，并检验由此生成的学者机器人是否能够以专家评估的质量执行核心学术功能。该提取流程采用八层提取方法与九模块技能架构，立足于局部封闭语料库分析。随后，学者机器人被部署于博士指导、同行评审、授课及小组式学术交流场景。专家评估由三位资深学者完成报告及聘任级别的综合评定。在保存的专家记录中，所有评审与指导报告均判定输出达到基准水平，聘任级别建议将两位机器人置于澳大利亚大学系统高级讲师及以上级别，多轮辩论条件下的恢复性小组评分显示学者A为7.9至8.9/10分，学者B为8.5至8.9/10分。一项研究学位生调查显示，尽管所有参与者已为前沿模型用户，但在信息可靠性、理论深度与逻辑严谨性方面仍表现出高绩效评分，七点量表上呈现显著天花板效应。我们将其称为"遗迹状态"：当出版系统使稳定推理架构变得可解读、可提取且廉价部署时，智力劳动的公共记录便成为其自身功能替代的原材料。由于实现这一转变的技术门槛在适度工程努力下已跨越，我们认为涵盖披露、知情同意、补偿及部署限制的保护框架窗口期即为当下——此刻部署仍为选择性而非基础设施性。