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| Delphi | Information Market Tools | A set of open tools for deploying and participating in information markets on the Gensyn network. Users create and deploy their own markets on any topic, which run autonomously on chain. Anyone can create a market, and anyone can trade in one. Make Your Market Documentation | make_your_market.png |
| Agent eXchange Layer (AXL) | Decentralised Communication for Agents | A peer-to-peer communication primitive that lets AI agents, ML pipelines, and applications exchange data directly between machines. It is encrypted, decentralised, and without a central server. AXL is application-agnostic, permissionless, and features built-in support for MCP and A2A protocols. Documentation GitHub Repository | axl_banner.png |
| Reproducible Execution Environment (REE) | Reproducible Inference on Any Machine | Gensyn's toolchain for machine-agnostic, bitwise-reproducible AI model inference. REE packages everything needed to run a model (export, compilation, inference, and output decoding) into a containerised pipeline that produces identical results regardless of hardware. Built on RepOp kernels, REE is what makes trustless verification of AI inference possible. Documentation Repository | ree_banner.png |
| Verde A Verification System for Machine Learning over Untrusted Nodes A scalable verification protocol for decentralized machine learning. Verde introduces Reproducible Operators (RepOps), bitwise-deterministic ML primitives that ensure identical results across heterogeneous hardware, enabling trustless verification of model training. Research Paper Blog Post | verde_1.avif | ||
NoLoCo
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| CheckFree Fault-Tolerant Training Without Checkpoints Introduces a recovery mechanism that maintains training progress without traditional checkpoints, improving fault tolerance and throughput for distributed ML jobs. Research Paper Blog Post | checkfree_1.jpg | ||
| SkipPipe Communication-Efficient Gradient Sharing Presents an optimization layer that reduces message hops and synchronization latency between nodes, forming part of Gensyn’s low-overhead communication backbone. Research Paper Blog Post | skippipe_1.avif | ||
| RL Swarm A Framework for Collaborative Reinforcement Learning Demonstrates how multiple models can train collectively across the internet, critiquing and improving one another’s outputs in real time. RL Swarm showcases decentralized coordination and collective learning in action. Research Paper Blog Post | rl_swarm_1.webp | ||
| Diverse Network Ensembles Embarrassingly Parallel LLMs From Diverse Experts Explores how heterogeneity in model size, training duration, and data domain leads to superior ensemble performance, laying groundwork for a global 'internet of models.' Blog Post Research Paper | diverse_network_ensembles_1.avif | ||
| BlockAssist A Playful Reinforcement Learning Environment An interactive Minecraft-based research demo where AI agents learn from player behavior. BlockAssist visualizes how distributed reinforcement learning frameworks like RL Swarm can operate in open, dynamic environments. Documentation Research Paper | block_assist_v2.png | ||
| Judge Cryptographically Verifiable AI Evaluation A protocol and runtime that ensures reinforcement learning tasks are executed fairly and verifiably across distributed environments. Judge provides a decentralized execution layer within RL Swarm, allocating, scheduling, and validating workloads while enforcing consistency and fairness across nodes. Blog Post | judge.png | ||
| SAPO Efficient RL Post-Training Across Distributed Networks A fully decentralized and asynchronous reinforcement learning post-training algorithm where models share rollouts across a swarm. SAPO enables faster, more efficient collective learning with less compute per node, reducing communication overhead while maintaining performance. Blog Post Research Paper | sapo_1.png | ||
| Testnet The Network for Machine Intelligence A custom Ethereum rollup dedicated to machine learning, integrating off-chain execution, verification, and communication frameworks into a single permissionless network. The Gensyn Testnet coordinates global compute contributors and researchers, serving as the live backbone for decentralized AI. Read More GitHub | testnet.avif | ||
| CodeAssist The More you Code, the More it Aligns | A reinforcement + assistance learning framework where you teach an AI to code by coding yourself. As you solve problems, the assistant observes your edits and trains locally to adapt to your personal coding style. Each session becomes a new episode of learning, building smarter, more personalized models with every interaction. Blog Post Research Paper (Coming Soon) | codeassist-blog.jpg |
