Abstract
What if organisms could write their own evolution? For 150 years, evolution has been understood as one-way: genes provide the rules, organisms respond to selection. But if organisms could learn during their lifetime, write that learned knowledge directly into their epigenome -- the heritable regulatory layer controlling gene expression -- and pass it to offspring, then knowledge would accumulate across generations instead of being rediscovered by each one. Such direct epigenomic inheritance would constitute genuine organismal agency: writing new information into heredity rather than passively receiving it. To test whether organisms actually benefit from this capability, we implement Phenopoiesis, a computational model in which organisms maintain two independent heritable layers: a genetic layer (inherited with mutation) and an epigenetic layer (inherited unchanged). We compare Phenopoiesis against Selection Only (no learning) and the Baldwin Effect (learning influences selection but is not inherited) across six learning tasks of increasing complexity: three simple (learn one pattern) and three compositional (learn two patterns simultaneously). On simple tasks all three mechanisms perform equivalently (p = 0.559): complexity is required for inheritance to matter. On learnable compositional tasks, Phenopoiesis outperforms the alternatives by 3-6x in fitness (F = 177-358, p < 0.001, Cohen's d = 3.44-4.89), with perfect reproducibility (s.d. = 0.0 on T+Plus across all 30 runs), indicating deterministic knowledge accumulation: offspring inherit their parents' learned solutions and add new learning on top. On a geometrically incompatible task all mechanisms fail equally, revealing an inherent limit. These results give the first direct computational test of Denis Noble's hypothesis that organisms possess read-write access to their own heredity, and show precisely when that access pays off.
More in this topicEvolutionary optimization searches for strong solutions through population-based variation, selection, and adaptation.
See all 291 papers → Oct 1, 2026
Learning can improve an individual's behavior, yet a population risks losing that experience whenever individuals die and are replaced. Inheriting learned preferences offers a way to preserve useful behavior across generations, raising a question for artificial populations: when does inheritance improve collective performance, and how can its benefits be measured fairly? The challenge is that inheritance changes not only offspring behavior but also survival, reproduction, and opportunities for further hereditary updates. Random controls with equal update magnitudes may therefore yield misleading comparisons if they alter different states or obey different stability constraints. We investigate this problem in a resource-limited artificial ecology, combining structured random controls with interventions on newborn preferences and the allocation of hereditary updates. Preserving the state structure of random updates substantially narrows the apparent inheritance advantage, while a conditional establishment-speed benefit remains. Preference erasure and faster-learning compensation support a contribution from reduced offspring relearning. Update allocation also changes the comparison: event quotas and common time cutoffs can reverse rankings, although they also change realized update amounts. With update count and cumulative magnitudes matched, staged release improves occupancy but does not achieve the prespecified establishment criterion. These findings provide a framework for distinguishing the value of inherited preferences from the effects of control design and update allocation, clarifying how inherited learning should be evaluated in artificial populations.
Xuening Wu, Lei Li, Shan Yu
Pfizer, Shanghai, China · Independent Researcher
Sep 15, 2026
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.
Lyes Saad Saoud
Independent Researcher Chicago, Illinois, USA; Abu Dhabi, UAE
Oct 15, 2026
Self-evolving language models train on tasks they generate, so every round must decide which updated model the next round inherits. Current loops such as R-Zero, Agent0, and Skill Self-Play make this decision through the task, admitting tasks that look learnable and inheriting every resulting update. We identify a selection-to-update gap in these loops, where a task that looks learnable need not produce a useful update. Most updates on verified tasks fail to make correct answers more likely, yet these loops inherit them all. HeirCommit freezes the updated model and uses fresh evaluation questions to decide whether to inherit it or retain the current model. EvoHeir separates parameter inheritance from experience inheritance, using current and archived experience to repair failures and preserve successes before final confirmation. Our analysis characterizes selection-induced optimism and decomposes regret into selection, absorption, and confirmation errors. HeirCommit cuts the harmful share of accepted updates by 68-83% relative to random inheritance at the same acceptance rate. Across four backbones, EvoHeir achieves the best mathematics and general-reasoning averages, raising the math average by 5.3% relative to the strongest baseline, Agent0. The framework lets useful experience persist even when the model that produced it is not inherited.
Haoyi Yang, Kshitij Mishra, Nils Lukas +1
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE