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RAYS Studio: Decentralized Federated OSINT Network
RAYS Studio is a revolutionary decentralized framework for open-source intelligence (OSINT) AI agents, built to solve the computational bottlenecks and privacy issues inherent in centralized LLM training. Standard agent architectures suffer from catastrophic forgetting when exposed to diverse localized datasets, while central servers struggle with massive training compute requirements. To solve this, RAYS Studio implements Federated Orthogonal Gradient Routing (FOGR) combined with Spectrally Bounded Zero-Gated Adapters (SB-ZGA). Local client devices execute complex OSINT workflows using RAYS-CORE, generating highly contextual, private task logs. When local fine-tuning is triggered, each edge client trains a low-rank adapter on its own hardware (utilizing GPU/MPS acceleration). We apply Singular Value Decomposition (SVD) constraints directly to the adapter weights, zeroing out dominant singular values to force the learning process into strict mathematical orthogonality. These localized, orthogonal adapter updates are securely submitted to the RAYS Studio daemon. The central daemon aggregates the weights, merges them with the base model, recompiles an optimized GGUF format, and hot-swaps the model instantly on a running llama.cpp server without downtime. By structuring learning spaces orthogonally, different clients can fine-tune on disparate OSINT targets simultaneously without erasing previously learned intelligence. The result is a highly scalable, privacy-first AI swarm capable of collective intelligence gathering across thousands of edge nodes.
13 Jul 2026
