Architecture & Technology
Core Architecture Components
1. Decentralized Data Ingestion & Integration
Currently supports secure image upload as the primary data ingestion method, with plans to expand to broader data types including on-chain and off-chain sources.
Enables integration with decentralized storage networks like IPFS and Arweave for storing and retrieving images in a tamper-proof, distributed manner.
Designed to incorporate smart contract-triggered events for future real-time ingestion pipelines.
Hybrid cloud and decentralized workflows planned for seamless expansion beyond image data.
2. Data Discovery & Indexing Engine
Utilizes on-chain metadata indexing combined with off-chain vector search and clustering algorithms (e.g., K-Means, HDBSCAN) to surface high-impact data points.
Embedding models (OpenAI, Cohere, Sentence Transformers) power semantic search and slicing, optimized for blockchain-specific datasets (e.g., transaction logs, smart contract events).
3. Collaborative Labeling & Annotation Layer
Web3-enabled annotation studio with support for:
Text classification
Sequence tagging
Multi-label tasks
Freeform feedback and rating (including RLHF scenarios)
On-chain governance for label schema versioning and task assignments, enabling decentralized and permissioned workflows.
Token-incentivized expert contributions to build and maintain trustworthy ground truths.
4. Reinforcement Learning from Human Feedback (RLHF) & Feedback Loop
Configurable reward models and preference datasets governed by smart contracts.
Reinforcement learning integrates human feedback on-chain and off-chain to iteratively improve model outputs (e.g., Proximal Policy Optimization).
Immutable audit trail on blockchain ensures transparent tracking of feedback, labels, model outputs, and retraining cycles.
5. Model Interface & MLOps Integration
API and smart contract-based access to integrate Javelin with existing training pipelines and blockchain oracles.
Compatible with popular frameworks (Hugging Face Transformers, OpenAI fine-tuning, LoRA/QLoRA) and allows deploying models as decentralized applications (dApps).
Artifact tracking, versioning, and provenance are managed via decentralized registries and MLflow-compatible tooling.
🔗 Blockchain-Native Features
Tokenized Incentives: Data providers, labelers, and reviewers earn tokens for contributions, enabling a sustainable and aligned ecosystem.
Decentralized Governance: Smart contract-based voting and approval workflows ensure transparent decision-making for dataset publishing, labeling policies, and model updates.
Secure & Compliant: Built-in support for encryption, zero-knowledge proofs, and compliance with data privacy regulations across jurisdictions.
With this architecture, Javelin AI bridges the power of generative AI and decentralized technology — enabling enterprises to harness precision AI sharpened by their unique data assets, securely and transparently on-chain and off-chain.
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