AI on Chain - ZoRobotics/Files GitHub Wiki

πŸ”— AI-on-Chain β€” The Verifiable Intelligence Layer

ZoRo AI-on-Chain is the protocol layer where all AI data, agent memory, and model operations are recorded, validated, and synced directly on blockchain. Built by ZoRobotics Technologies, this mechanism forms the cryptographic backbone of the entire AI ecosystem β€” ensuring transparency, data lineage, and optional user-permissioned sharing in machine learning workflows.

The ZoRo protocol is not deployed on a third-party chain. It is being developed as a native L1 blockchain, designed specifically for AI use cases: high-throughput memory sync, secure agent interactions, and decentralized training pipelines. This structure supports fast block finality, minimal gas latency, and modular smart contract logic β€” tailored to AI validation and data state transitions.

🧬 Core Mechanisms

1. 🧱 Solidity-Based Memory Encoding

The on-chain logic is defined through a set of Solidity contracts that:

  • Receive and log agent actions, task submissions, and inference results
  • Commit proofs of annotation, validation, and arbitration outcomes
  • Link encrypted user metadata to wallet addresses in zk-backed memory chains

The L1 structure is optimized to minimize verification time while maintaining immutability. Each confirmed task or memory event is processed through this smart contract stack and connected to the user’s on-chain agent profile.

2. πŸ“¦ Metadata Storage via IPFS (Pinata Cloud)

Raw or structured content (e.g., annotated text, biometric logs, vector sequences) is stored off-chain using Pinata Cloud (IPFS layer). Each on-chain task log contains a hash-pointer to the external data blob, ensuring:

  • Content immutability
  • Separation of sensitive and structural payloads
  • Scalable integration of multi-modal data without overloading on-chain storage

The metadata format supports tagging, timestamps, annotation types, and validator context.

3. 🧾 Meta-Profile System

Each wallet address in the network is linked to a dynamic Meta-Profile, which includes:

  • Encrypted memory slots (per interaction or task)
  • Historical inference sessions
  • Validation performance and reputation score
  • Behavioral or biometric input patterns (optional)

Meta-profiles allow agents and models to operate across devices and platforms while retaining personalized logic and session history. These profiles form the core of persistent agent behavior.

4. πŸ” Data Sharing Permissions

ZoRo implements a signature-based encryption system for sensitive data sharing. Users may choose to:

  • Sign encrypted memory slots to privately share their agent data with selected training projects
  • Grant public access to selected memory domains for open training (zero-annotation learning)
  • Keep their entire memory space private and local, accessible only via their wallet signature

This logic is enforced by smart contracts that record data access rules and enable traceable disclosures for compliant projects.

πŸͺ™ Native Payments with ZORO Token

Every operation that commits data to the ZoRo L1 chain β€” including memory updates, public profile publishing, or agent state sync β€” incurs an on-chain transaction. These actions are paid in the ZORO token, which serves as the native gas and coordination unit across the network.

Users publishing data, opening access to meta-profiles, or streaming agent events to the chain will use ZORO to cover these costs, ensuring economic alignment between participation, compute usage, and data footprint.

β†’ Learn more in ZORO Token

🧭 Goals of AI-on-Chain

  • Replace unverifiable annotation workflows with cryptographic proofs
  • Enable persistent, wallet-bound memory with zk-backed access control
  • Build a structured public memory pool for open AI training
  • Let users participate directly in the training loop β€” not just as annotators, but as data owners

AI-on-Chain redefines how machine learning agents interact with humans, how data flows across decentralized infrastructure, and how future AI models can be trained on permissioned, verifiable, and user-consented signals.

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