An Evolutionary Stream-of-Consciousness Framework for Large Language Models: Adaptive Cognitive Input Structures via Motivational Genes

Abstract This paper proposes an evolutionary framework for organizing the cognitive inputs of large language models (LLMs) through an adaptive intermediate layer called a stream-of-consciousness structure. Rather than relying on manually engineered prompts, the framework assembles a hierarchical context tree from a heterogeneous information pool — including user prompts, contextual memory, associative expansions, and internal reflections — prior to language model inference. The organization of this structure is governed by motivational genes, latent structural configurations that encode selection, ordering, and weighting biases over information elements. These genes evolve through a population-based mechanism driven by implicit user feedback signals (e.g., follow-up behavior, repetition, corrections, and praise), without modifying the base model’s parameters. We formalize the framework with explicit definitions, algorithms, and formal properties, and provide a fully specified prototype with concrete satisfaction estimation, fitness update rules, and an adaptive mutation schedule. All components are defined at a level of detail sufficient for reproducible implementation. The paper is positioned as a conceptual framework with an operational prototype specification; empirical validation in deployment settings is identified as a primary direction for future work.