发表机构
Changchun University of Science and Technology（长春理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究揭示自主科学发现中因错误配对导致的虚假科学归纳现象，提出错误连贯性为预算错配主因，并引入隔离与分流机制以保障实验有效性。
AI 中文摘要
闭环发现系统日益自主地执行实验并更新决策，将记录完整性转化为实验装置的一部分。我们表明，当合法的物理对象与测量被错误配对时，会产生虚假科学归纳，驱使神经替代模型忠实地学习记录诱导的关联，而这些关联并不对应真实的对象-结果关系，同时边缘数据分布保持不变。在绿色荧光蛋白适应性和材料带隙预测循环中，连贯的配对错误绑定系统性地将实验预算引向低性能区域，而相同数量的随机交换则影响可忽略。这些观察表明，在测试的循环中，错误连贯性而非原始错误频率，是控制这种预算错配的主要变量。由此产生的绑定可识别性边界支持监测轴隔离和反馈冲突分流，在执行前拦截过度集中的提议，并隔离被破坏的假设轴。
英文摘要
Closed-loop discovery systems increasingly execute experiments and update decisions autonomously, turning record integrity into part of the experimental apparatus. We show that false-science induction arises when legitimate physical objects and measurements are paired incorrectly, driving neural surrogates to faithfully learn record-induced associations that do not correspond to the true object-outcome relationship while marginal data distributions remain unchanged. Across green fluorescent protein fitness and materials band-gap prediction loops, coherent paired misbinding systematically redirects experimental budgets toward low-performing basins, whereas same-volume random swaps have negligible effects. These observations identify error coherence, rather than raw error frequency, as the primary variable controlling this budget misallocation in the tested loops. The resulting binding identifiability boundary supports monitored-axis quarantines and feedback-conflict triage, which intercept over-concentrated proposals before execution and isolate the corrupted hypothesis axis.
