# Core Components

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## The Four Layers

The Gensyn network is built on four foundational layers that together provide the infrastructure for open machine intelligence from reproducible execution to decentralised coordination.

Each layer contributes a distinct capability and is represented by active products, primitives, and research across the Gensyn ecosystem.

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Read the [Protocol Overview](https://docs.gensyn.network/) here.
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### Reproducible Execution

> Ensuring that the same model and inputs produce the same outputs, regardless of hardware.

For AI to operate in open, trustless environments where third parties need to independently verify that a computation was performed correctly, execution must be reproducible across machines.

The [Reproducible Execution Environment (REE)](https://app.gitbook.com/s/jHECdpSAZDuPfU2oZmM2/) is Gensyn's toolchain for machine-agnostic, bitwise-reproducible AI model inference. It packages everything needed to run a model: **\[1]** export, **\[2]** compilation, **\[3]** inference, and **\[4]** output decoding into a containerised pipeline that produces identical results regardless of which hardware it runs on.

REE is built on RepOp kernels: purpose-built operators that guarantee bitwise-identical outputs across different hardware, parallelism configurations, and run orders. This is what makes trustless verification possible. If two machines can't agree on the same result, you can't verify anything.

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*Related Research*
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[SAPO](https://blog.gensyn.ai/sapo-efficient-lm-post-training-with-collective-rl/): A reinforcement learning algorithm designed for stable policy optimisation across distributed nodes.
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### Trustless Verification

> Checking and reaching agreement on work performed, without relying on a central authority.

Once execution is reproducible, work performed across the network can be verified without trusted intermediaries. The verification layer provides a system for detecting and resolving disagreements between participants so the network can always reach consensus on correct results.

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*Related Research*
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[Verde:](https://blog.gensyn.ai/verde-a-verification-system-for-machine-learning-over-untrusted-nodes/) A library of bitwise-reproducible ML operators (RepOps) that provides the theoretical framework underpinning reproducible execution.

[Judge:](judge:https://blog.gensyn.ai/introducing-judge/) A cryptographically verifiable AI evaluator that enforces correctness at the application layer, demonstrating how verification works in practice for real-world AI workloads.
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### Peer-to-Peer Communication

> Direct, encrypted communication between machines over the internet, without a central server.

The [Agent eXchange Layer (AXL)](https://app.gitbook.com/s/jHECdpSAZDuPfU2oZmM2/agent-exchange-layer) is a peer-to-peer communication primitive built by Gensyn. It provides an encrypted, decentralised communication layer where AI agents, ML pipelines, and applications can exchange data directly between machines.&#x20;

AXL is application-agnostic, meaning it moves bytes between peers and has no opinion about what those bytes mean, and features built-in support for MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication.

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*Related Research*
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[NoLoCo:](https://blog.gensyn.ai/noloco-training-large-models-with-no-all-reduce/) Replaces the costly all-reduce step with a low-communication gossip approach for distributed training.

[CheckFree:](https://blog.gensyn.ai/checkfree-fault-tolerant-training-without-checkpoints/) Enables fault-tolerant recovery without checkpointing, reducing compute overhead.

[SkipPipe:](https://blog.gensyn.ai/skippipe-a-communication-efficient-method-for-decentralised-training/) An efficient gradient-sharing algorithm that minimises message hops across the network.
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### On-Chain Coordination

> Bringing participants and applications together on a shared, permissionless ledger.

The coordination layer provides the on-chain infrastructure for applications on the Gensyn network.&#x20;

Today, this is where information markets deployed through Delphi run, with market creation, participation, and resolution all happening on chain.

As the network matures, this layer will expand to support broader economic coordination: participant identification, incentive alignment, and permissionless payment settlement across the ecosystem.

### Getting Started

Explore [Delphi](https://app.delphi.fyi/) to create or trade in information markets, try [AXL](https://app.gitbook.com/s/jHECdpSAZDuPfU2oZmM2/agent-exchange-layer) to build peer-to-peer agent applications, or join our [Discord](https://discord.com/invite/gensyn) community to connect with the team and other contributors.


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