7.0 Overview

At this depth, the mesh no longer behaves like discrete software components; it operates as a self-referential ecosystem.

Traits become patterns of adaptation. Nodes rewrite themselves through telemetry, consensus, and observed environmental stress.

The system’s goal: reach stable dynamism — ever-changing, never broken. [L1]https://eng.libretexts.org/Bookshelves/Mechanical_Engineering/Introduction_to_Autonomous_Robots_(Correll)/02:_Locomotion_and_Manipulation/2.02:__Static_and_Dynamic_Stability [L2]https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007659

[U1] : https://istio.io/latest/about/service-mesh/

7.0.1 Evolutionary State Loop

Every epoch completes a Reflect–Adapt–Reform cycle:

REFLECT   → collect global metrics
ADAPT     → modify traits, frequencies, and topologies
REFORM    → deploy new stable configuration

Rust code orchestrates this through dynamic trait re-binding and runtime module hot-swapping.

pub trait Evolvable {
    fn reflect(&self) -> Metrics;
    fn adapt(&mut self, context: &Metrics);
    fn reform(&mut self);
}

7.1 Memory of the Mesh (Temporal Persistence) : [U2]

Each node keeps a temporal shadow — a rolling window of behavioral patterns — to inform future corrections.

[U2] : https://cloud.ibm.com/docs/vpc?topic=vpc-about-vpc

7.1.1 Shadow Persistence

struct TemporalMemory { snapshots: Vec<SnapshotFrame> }
impl TemporalMemory {
    fn learn(&mut self, frame: SnapshotFrame) {
        self.snapshots.push(frame);
        if self.snapshots.len() > 1000 { self.snapshots.remove(0); }
    }
}

7.1.2 Predictive Feedback

Temporal deltas become a primitive form of distributed learning: