GeNeSIS V: An Iterative Engineering Case Study in Multi-Paradigm Articial Life Architecture

Abstract

GeNeSIS I–V: An Iterative Engineering Case Study in Multi-Paradigm Artificial Life Architecture presents the development of GeNeSIS, a five-generation artificial-life research project focused on building increasingly integrated computational systems for cognition, evolution, biology, chemistry, environment, social structure, and culture. Rather than treating the project as a conventional scientific study, the paper presents it as an engineering case study documenting how successive architectural ideas were developed, unified, tested, and evaluated. The first four iterations establish the progression of the project. GeNeSIS I explored causal-emergence-based self-modeling and behavioral milestones. GeNeSIS II introduced policy-gradient learning through PPO together with recurrent memory and an inheritance mechanism intended to preserve aspects of parental state. GeNeSIS III moved toward model-based planning using a Dreamer-style world model, allowing agents to evaluate imagined trajectories. GeNeSIS IV replaced the conventional neural agent core with a complex 64-dimensional Hilbert-space representation, using unitary evolution, Born-rule action selection, Hamiltonian-based learning, associative memory, Gödel-numbered behavioral programs, and phase synchronization. GeNeSIS V builds directly on this fourth iteration and introduces the central architectural idea of the paper: the same 64-dimensional mathematical structure is used to represent an agent's cognition, genome, and behavioral encoding. The choice of 64 dimensions is motivated by the 64 codons of the standard genetic code. Instead of maintaining separate representations for cognitive state and genetic information, the system derives a symbolic genome from the eigenbasis of the agent's Hamiltonian. Consequently, changes to the underlying Hamiltonian simultaneously affect the agent's cognitive dynamics and its encoded genotype. The paper argues that this provides a unified substrate in which several normally independent mechanisms can interact through a shared representation. Around this cognitive-genetic core, GeNeSIS V implements independent physical, biological, chemical, ecological, social, and cultural mechanisms. These include Gray–Scott reaction-diffusion for pattern formation, Arrhenius kinetics for temperature-dependent reactions, Lotka–Volterra dynamics, autocatalytic chemistry, procedural terrain and resource fields, cultural transmission based on a retention model, tribal and diplomatic structures, Kuramoto synchronization, associative memory, Gödel encoding, and procedural growth grammars. The architecture therefore attempts to combine multiple established mathematical mechanisms within one executable artificial-life environment rather than introducing a single new simulation rule. A major part of the paper concerns engineering constraints and verification. The system was designed around a 2 GB memory ceiling, with procedural terrain, bounded populations, capped history buffers, and a lightweight environment model used to control memory consumption. The implementation consists of twelve Python modules and a Streamlit interface. Verification includes invariant checks for state normalization and Hamiltonian Hermiticity, conservation tests for chemical reactions, topological tests using Betti numbers, regression validation of the causal-model component, cross-module integration testing, and end-to-end execution of the deployed application. The results deliberately include both successful mechanisms and failures. Population regulation behaved as intended, and a representational error in the chemical subsystem was identified and corrected by separating molecular-species concentrations from bulk elemental composition. Calibration problems in the breakthrough-detection layer and an asymmetric statistical comparison were also detected through targeted testing and corrected. More importantly, the system failed to produce the intended tribal and phylogenetic diversification under its specified parameters. The paper traces this failure to the near-identical spectral structure of agents' Hamiltonians, which causes the similarity measures governing affiliation and clustering to remain too high. Rather than changing the parameters simply to obtain the desired behavior, the study retains and reports the failure as a limitation of the current architecture. The paper consequently makes a deliberately narrow claim. GeNeSIS V demonstrates the engineering feasibility of placing cognitive state, genetic representation, and behavioral encoding on a common 64-dimensional substrate while maintaining independently testable subsystems. It does not claim to demonstrate consciousness, subjective experience, general intelligence, or a solved model of open-ended evolution. The broader contribution is therefore the architectural design itself, together with the verification discipline used to evaluate a complex artificial-life system and the explicit documentation of mechanisms that succeeded, failed, or remain incomplete.