Deterministic and efficient AI through targeted evolutionary computing.

Get Started with Oscilon

Oscilon introduces Evolutionary Adaptive Intelligence (EAI): a novel approach that delivers reliable, deterministic AI by identifying error-prone nodes and applying focused genetic mutations guided by strict fitness thresholds. Designed for high-stakes domains like healthcare and edge computing, Oscilon enables efficient optimization on consumer-grade and embedded hardware without probabilistic errors.

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Run example (approved researchers)

// cpp


#include <oscilon/core.h>
#include <oscilon/evolution.h>
int main() {
    oscilon::Network net = oscilon::load("baseline.osm");
    oscilon::ErrorScanner scanner(net);

    oscilon::Mutator mutator(net);
    oscilon::FitnessEvaluator fitness(threshold = 0.001);  // Strict determinism

    for (auto node : error_nodes) {
        mutator.apply_ga_mutation(node);
        if (fitness.evaluate(net) > fitness.threshold) {
            mutator.commit();  // Only beneficial changes retained
        }
    }
    net.save("optimized_deterministic.osm");
}

Solve real-world problems with deterministic AI

Security and Defense

Oscilon’s scalar-level mutations and strict thresholding eliminate hallucinations, ensuring convergence guarantees critical for real-time link message classification, threat detection, and secure data fusion on edge devices.

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Edge Computing

Targeted sparse optimization achieves 10-50x fewer operations than traditional neuro-evolution, enabling low-latency TDL inference in resource-constrained tactical environments.

Read benchmarks (approved researchers)

Heterogeneous Acceleration

Parallel mutation evaluation via ROCm/HIP, DirectML, and Metal. Scale deterministic genetic-evolution across ruggedized hardware and mobile platforms.

View accelerator guide

What's new in Oscilon

Updates from the core team and approved researcher contributions.

Oscilon v0.1 research preview

Initial release – December, 2025

Core sparse node identification, targeted GA mutations, deterministic fitness functions, and heterogeneous backends now available to approved researchers.

More updates (private repository)