机器合子：人工种系-体细胞智能体在学习前的因果双亲遗传
Machine Zygote: Causal Biparental Heredity Before Learning in a Germline--Soma Artificial Agent
AI总结：
本文提出机器合子架构，通过种系重组与体细胞发育，在无学习条件下因果验证了双亲遗传，并提供了干预中心框架与可复现基准。
AI中文摘要：
人工个体发育、发育编码、机器人繁殖和继承控制器是既定的研究方向，但一个更具体的问题仍然存在：新生的智能体能否在学习前表现出可测量的双亲遗传，且这种依赖性能否被因果地隔离，而不仅仅是从亲子相似性中推断？我们引入了机器合子（Machine Zygote），一种计算性的种系-体细胞架构，旨在检验这一问题。两个亲本种系被独立突变并重组为一个合子，该合子参数化了一个初始通用的八模块体细胞的发育，随后该体细胞被冻结并在无学习条件下评估。一项预注册的4×4双列杂交实验（共640个子代）在霍尔姆校正后显示，六项行为特征中有五项存在显著的父本和母本依赖性，亲本及交互成分在五个主要特征中占建模方差的36%至53%。在匹配背景干预中（n=60），在保持重组和随机背景固定的情况下，替换一个亲本种系导致表型偏移，对于两个亲本通道，六项特征中有五项超过了同亲本重新突变对照。重组还导致速度和步频方面产生过多的越界子代。一项预注册的发育依赖性假设未得到支持：准静态无动力学消融在改变亲本方差结构的同时保持了平均表型分布。因此，该研究支持该模拟中因果性的学习前双亲遗传，但不支持更强的主张，即循环发育动力学是必要的。它并未确立物理遗传、生物遗传学或自主进化。其贡献在于一个以干预为中心的框架和可复现的基准，用于分离遗传、发育、随机变异和出生后学习。
英文摘要：
Artificial ontogeny, developmental encodings, robot reproduction, and inherited controllers are established research directions, yet a narrower question remains: can a newborn artificial agent exhibit measurable biparental heredity before learning, and can that dependence be isolated causally rather than inferred only from parent-offspring resemblance? We introduce Machine Zygote, a computational germline-soma architecture designed to test this question. Two parental germlines are independently mutated and recombined into a zygote that parameterizes development of an initially generic eight-module soma, which is then frozen and evaluated without learning. A preregistered 4 x 4 diallel of 640 offspring shows significant dam and sire dependence for five of six behavioral traits after Holm correction, with parental and interaction components accounting for 36-53 percent of modeled variance across five principal traits. In matched-background interventions (n=60), substituting one parental germline while holding recombination and stochastic background fixed causes phenotype shifts exceeding a same-parent re-mutation control for five of six traits for both parental channels. Recombination also yields excess transgressive offspring for speed and gait frequency. A preregistered developmental-dependence hypothesis is not supported: a quasistatic no-dynamics ablation preserves the mean phenotype distribution while altering parental variance structure. Thus the study supports causal biparental pre-learning heredity in this simulation, but not the stronger claim that recurrent developmental dynamics are necessary. It does not establish physical heredity, biological genetics, or autonomous evolution. The contribution is an intervention-centered framework and reproducible benchmark for separating heredity, development, stochastic variation, and post-birth learning.
