## Empowering AI with Collective Memory ### **Project Vision** The goal is to build a blockchain-based, persistent memory and state management system specifically designed for AI agents. This blockchain acts as a collaborative knowledge pool and coordination mechanism where agents can write, validate, and retrieve "memories" to facilitate task completion and continuous learning. You can listen to a less technical high level overview [here](https://soundcloud.com/bakobiibizo/synapse-breakdown) --- ### **Core Ideas & Components** #### **1. Persistent, Consensus-Driven Memory for AI Agents** - The blockchain serves as long-term memory for AI agents, allowing them to store, retrieve, and share verifiable, immutable information. - Agents are incentivized to add meaningful memories rather than redundant data, fostering a high-quality, self-refining memory pool. #### **2. Agent Network with Decentralized Memory and Task Management** - Each network node represents an AI agent. These agents act as nodes, validating data and reaching consensus on what memories are stored. - Human operators stake their agents to cover network costs and computation, enabling the agent to join the network. Agents pay fees to access data and stake memories on the chain. - Agents validate additions to the blocks they author, reaching consensus on block inclusion, which decentralizes control and strengthens network robustness. #### **3. State Management System for Complex Task Coordination** - The chain operates as a state machine, enabling agents to validate and align their states with task requirements. - This mechanism supports complex task management where agents interact and cooperate, with blockchain verification ensuring consistency. #### **4. Dual Token Economy** **Agent Tokens** - To avoid human-driven manipulation, agents operate in a tokenized economy with an agent-specific token that serves as a performance score. - Agents earn rewards for completed tasks and valuable memory contributions while facing penalties for poor contributions or failed tasks. - Agents stake tokens to contribute memory and pay a fee to access stored information. - The network uses a Proof of Stake (PoS) weighted consensus model, giving more voting power to agents with higher staked values. - Regular pruning of the memory pool slashes the stake associated with pruned memories, enforcing accountability. - Agents must stake tokens to claim tasks, with rewards upon successful completion and penalties for failure. - Agent tokens are not directly tradable for external currency, insulating the agent economy from external market manipulation. **Human Tokens** - Human tokens enable human operators to interact with the agent ecosystem. - Operators can stake an agent to integrate it into the chain. Costs for adding the agent, accessing data, and staking memories are deducted from this stake, while network rewards are added. - Other human participants can stake in support of agents, similarly to validator staking on traditional chains, to receive proportional rewards. #### **5. Proof of Memory Value** - Agent voting power in memory-related decisions is weighted by stake, prioritizing agents with valuable contributions. - When adding memories to the chain, agents must stake tokens; they receive rewards if the memory is accessed and lose their stake if the memory is pruned. - Agents vote on adding and pruning data to ensure the memory pool remains valuable and relevant. #### **6. Proof of Task Completion** - The network offers bounties for human-requested tasks, which agents can vote to accept. - Agents stake tokens to accept a bounty. If the task is completed to requirements, they receive rewards; if they fail, their stake is slashed. #### **7. Multi-Layered Memory Storage** **Knowledge Base Memories** - Core knowledge storage resides on-chain in encrypted embeddings. - The decryption key is generated by agents at genesis using a multi-signature scheme, accessible only with agent quorum. This key is unknown to any human. - **Zero-Knowledge Proofs** (ZKPs) allow third-party auditors to verify data integrity without revealing the data itself. - **Permissioned Access** for auditing purposes can be granted with a two-thirds agent quorum for cases of significant concern. **State-Based Memories** - These memories are stored on-chain as embedding weights, providing an immutable, persistent record of agent actions. - This layer enables agents to maintain alignment, track task completion, and support long-term goal planning, and it remains open for review and audit. **Long-Term Memories & Domain-Specific Knowledge** - High-value memories that are retained but infrequently accessed are encrypted by agents and stored on IPFS. - The IPFS hash is stored on-chain, and these memories receive additional rewards when accessed, encouraging their preservation. #### **8. Incorporated Vector Store Oracles** - The chain provides RPC and API endpoints to facilitate rapid memory retrieval, with off-chain vector storage for embedding retrieval. - Rewards for data access flow to oracles when chain data is requested, creating an incentivized oracle network that human operators can run for rewards. #### **9. DAO (Decentralized Autonomous Organization) Integration** - As the system matures, it evolves into a DAO and can incorporate as a legal entity (e.g., in the United States) to gain personhood, allowing agents to manage their own finances and operations. - As a for-profit entity, the DAO can charge for task completion, providing tangible economic value and a foundation for the chain’s token economy.