Genomebook: Mendelian inheritance as a structured parameterisation layer for LLM agent populations

Abstract

Large language model (LLM) agents are typically deployed as clones: identical copies of a single configuration with no mechanism for heritable variation or population-level dynamics. Here we introduce Genomebook, a designed evolutionary system that encodes 26 behavioural traits across 60 diploid loci using additive, dominant, and recessive inheritance models. Twenty founder agents, each defined by a personality profile (SOUL.md) and a compiled genome (DNA.md), reproduce sexually via Mendelian segregation with de novo mutation at a base rate of 0.1% per locus per generation (3x at cognitive, immune, and metabolic hotspots). A registry of 20 synthetic conditions with defined penetrance and fitness costs introduces centrally imposed selective pressure through a compatibility scoring function. Over a single run of eight generations (626 agents, 792 social network posts), we observe trait trajectories consistent with the encoded selection rules: leadership rose from 0.525 to 0.710 under dominant inheritance, obsessive focus fell from 0.775 to 0.601 under fitness cost penalisation, and longevity declined from 0.463 to 0.209. Vocabulary diversity declined from 0.42 to 0.19, and topic inheritance between parents and offspring reached 83%, though these patterns may reflect prompt conditioning rather than purely genetic causation. Agents referenced family members spontaneously, consistent with kinship information present in the DNA.md system prompt and the LLMs capacity for narrative elaboration. Replicated simulations (20 independent runs across three conditions) confirm that trait trajectories are consistent across seeds under standard selection, while a random mating ablation produces attenuated trends with wider variance. A non-genetic baseline (random trait assignment, no inheritance) produces flat trajectories converging to population means, confirming that the observed dynamics require genetic architecture and cannot be reproduced by unstructured parameter variation. These results establish genetic architecture as a structured, auditable, and heritable parameterisation layer for LLM agent behaviour.