Chapter 11: Neosis Evolution

The central theme of Neosis evolution is the natural emergence of higher-order modal and conceptual representations as a means of survival. Neos that develop the ability to perceive, abstract, and organize environmental patterns at increasingly sophisticated levels gain a fundamental survival advantage. This advantage manifests through more accurate predictions, which yield greater energy (Nex) through Sparks, enabling further structural exploration and refinement.

The evolutionary mechanisms that enable this process—predictive advantage, mutation, culling, and diversity pulses—operate continuously and without external guidance. There is no predefined fitness function or optimization target. Instead, evolution emerges naturally from the operational dynamics of individual Neos interacting with a structured environment. This chapter examines how these mechanisms collectively produce the open-ended evolution of increasingly sophisticated cognitive structures, with a particular focus on how modal and conceptual representations emerge as adaptive solutions to the fundamental challenge of survival through accurate prediction.

11.1 Predictive Advantage as Implicit Fitness

In Neosis, the NeoVerse does not intentionally reward agents. Instead, Sparks quantify the natural survival advantage of accurate prediction. When a Neo's output $Y_t$ closely matches the future percept $U_{t+1}$ under a distance-based accuracy measure, it gains Nex. This is the computational analog of biological organisms whose better world-models yield more resources, safety, or opportunities. Thus, Spark is a proxy for fitness, and accurate prediction becomes the sole driver of evolutionary success.

11.2 Mutation as Structure Exploration

Structural and parametric mutations occur through Evo using primitives (node$^+$, node$^-$, edge$^+$, edge$^-$, param$^f$). Mutations consume Nex, so only Neos with surplus energy can explore more aggressively. Low-Nex Neos mutate rarely or not at all. This creates an intrinsic coupling between performance and evolutionary plasticity: only successful Neos evolve structurally, while weak Neos gradually stagnate.

11.3 Culling of Low-Performance Neos

At fixed intervals, a fraction of Neos with the lowest recent Nex or worst average Spark performance are removed from the population. This prevents stagnation by eliminating Neos that have neither died nor evolved but remain energetically weak. Continuous culling ensures that computational resources are spent on promising evolutionary trajectories.

11.4 Catastrophic Diversity Pulses

Rare large-scale events remove a random subset of the population with a bias against low-Nex Neos. Unlike culling, which removes chronically weak individuals, diversity pulses help escape evolutionary dead-ends. They reintroduce exploratory pressure, destabilize over-converged lineages, and allow new structural motifs to emerge.

11.5 Spark-Driven Concept Emergence

Modal and conceptual representations arise naturally because they reduce prediction error. The NeoVerse uses distance-based accuracy metrics to compute Sparks; thus, any internal structure that compresses, abstracts, or organizes world signals into predictable patterns yields higher Nex. Neos therefore evolve perceptual expansion, internal memory, and higher-level abstractions without explicit guidance or extra rewards.

11.6 Open-Ended Evolutionary Dynamics

Together, predictive advantage, mutation, culling, and diversity pulses create an open-ended evolutionary system. High-Nex Neos explore new structural configurations and dominate the population. Low-Nex Neos vanish through culling or catastrophic events. As long as the NeoVerse presents structured, temporally rich, and multimodal patterns, Neos will progressively evolve more sophisticated internal computation and world-models.