Shivya: The Non-Dual Distributed Computing Substrate

Shivya is a bare-metal, zero-dependency, edge-native distributed substrate. It discards dualistic, clock-synchronized consensus models (e.g. Paxos, Raft, Nakamoto Consensus) in favor of a continuous, thermodynamic geometric manifold driven by Discrete Exterior Calculus and Variational Free Energy minimization.

The 5-Layer Architectural Stack

graph TD
    Layer4["Layer 4: Turing Morphogenesis (Topology Adaption)"] --> Layer3["Layer 3: Onsager Ensemble (Reciprocal Flow Diffusion)"]
    Layer3 --> Layer2["Layer 2: Morphic Core (Metamorphic VM & State Expansion)"]
    Layer2 --> Layer1["Layer 1: Gibbs Flux (Variational Active Inference)"]
    Layer1 --> Layer0["Layer 0: Hodge Mesh (Simplicial Boundary Reconciler)"]

Layer 0: Topological Fabric (shivya-hodge)

• Core Abstraction: Simplicial State Complexes and Discrete Exterior Calculus (DEC).
• Function: Solves structural boundary flow equations using an iterative Conjugate Gradient solver. It partitions concurrent network partitions into a gradient flow (non-conflicting mutations) and a curl flow (rotational conflict loops), projecting out the curl to arrive at consistent states deterministically without time locks.

Layer 1: Predictive Homeostasis (shivya-flux)

• Core Abstraction: Variational Free Energy Principle (FEP).
• Function: Represents nodes as Active Inference Agents bound by statistical Markov Blankets. Nodes minimize Variational Free Energy ($F$) via continuous gradient descent over internal belief parameters to adapt to non-stationary sensorimotor telemetry.

Layer 2: Autotelic Morphic Core (shivya-morphic)

• Core Abstraction: Metamorphic VM Hot-Swapping & State Space Expansion.
• Function: Evaluates structural update loops inside a sandboxed, stack-allocated Register VM with strict instruction cycle budgets. When moving average free energy breaches novelty thresholds, the node expands its generative state dimensions (e.g., from 2D to 3D) and rewrites its execution bytecode.

Layer 3: Thermodynamic Collective Ensemble (shivya-onsager)

• Core Abstraction: Onsager Reciprocal Relations & Game-Theoretic FEP.
• Function: Regulates parameter and workload migration across blankets via symmetric conductance couplings ($L_{ij} = L_{ji}$). Computes global Collective Free Energy ($\mathcal{F}_{\text{collective}}$) by resolving Harsanyi dividends recursively over adjacent neighbor coalitions to enforce cooperative synergy.

Layer 4: Morphogenetic Pattern Substrate (shivya-turing)

• Core Abstraction: Non-linear Graph Reaction-Diffusion & Network Plasticity.
• Function: Solves activator-inhibitor partial differential equations using Runge-Kutta 4th Order (RK4) integration with dynamic CFL stability guards. High-stress activator hotspots trigger zero-allocation vertex mitosis (node splits), while low-utility nodes undergo apoptosis (culling) to optimize global resource usage.

Rust Integration Example

use shivya::hodge::complex::SimplicialStateComplex;
use shivya::flux::model::GibbsFluxAgent;
use shivya::morphic::{DynamicGibbsAgent, MorphicHotSwapper, Expr};
use shivya::onsager::OnsagerCollectiveEnsemble;
use std::thread;
use std::time::Duration;

fn main() {
    println!("Initializing Shivya Substrate...");

    // 1. Establish initial 3-node topology
    let mut adjacent_nodes = vec![
        vec![1, 2], // Node 0 neighbors
        vec![0, 2], // Node 1 neighbors
        vec![0, 1], // Node 2 neighbors
    ];

    // 2. Set up Gibbs Active Inference Agents
    let create_agent = |mu_prior: f64| {
        DynamicGibbsAgent::new(
            2, 1, 2,
            vec![mu_prior, 0.0],
            vec![vec![10.0, 0.0], vec![0.0, 10.0]],
            vec![vec![1.5, 0.0], vec![0.0, 1.5]],
            vec![vec![0.1, 0.0], vec![0.0, 0.1]],
            vec![vec![0.0], vec![0.0]],
            vec![vec![0.0], vec![0.0]],
            vec![0.0, 0.0],
            vec![vec![1.0, 0.0], vec![0.0, 1.0]],
            5.0, // Novelty threshold
        )
    };

    let agents = vec![
        create_agent(0.1),
        create_agent(0.2),
        create_agent(0.15),
    ];

    // 3. Construct the Onsager Collective Ensemble
    let mut ensemble = OnsagerCollectiveEnsemble::new(agents, adjacent_nodes, 0.5);

    // 4. Run the homeostatic execution loop inside a runtime thread
    let handle = thread::spawn(move || {
        let observations = vec![
            vec![1.2, 0.8], // Node 0 sensory inputs
            vec![2.0, 1.1], // Node 1 sensory inputs
            vec![1.5, 0.9], // Node 2 sensory inputs
        ];

        for step in 1..=5 {
            let collective_f = ensemble.step(
                &observations,
                0.1,   // Learning rate
                10,    // Max belief iterations
                1e-4,  // Convergence tolerance
                0.1    // Onsager migration rate
            );
            println!("Step {} -> Collective Free Energy: {:.6}", step, collective_f);
            thread::sleep(Duration::from_millis(50));
        }
    });

    handle.join().unwrap();
    println!("Homeostatic loop terminated stably.");
}

Crate Layout & Distribution namespaces

All modules are zero-dependency, stack-allocated, and target WebAssembly (wasm32-unknown-unknown):

• crates/shivya-hodge - Layer 0 Simplicial exterior calculus
• crates/shivya-flux - Layer 1 Homeostatic Active Inference agent
• crates/shivya-morphic - Layer 2 Sandboxed metamorphic register VM
• crates/shivya-onsager - Layer 3 Thermodynamic multi-agent ensemble
• crates/shivya-turing - Layer 4 Network reaction-diffusion morphogenesis
• crates/telemetry_wasm - Unified Substrate WASM telemetry bindings

License

This framework is distributed under the terms of both the MIT license and the Apache License (Version 2.0). See LICENSE-MIT and LICENSE-APACHE for details.