AI Learns Like Living Things: New Method Lets It Evolve on Its Own

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.