Decrypting the D.A.T.A framework: How to rebuild the multi-chain interactive ecosystem?

Author: Haotian

Recently, @carv_official released a set of D.A.T.A framework and standards. As the name suggests, Virtual's G.A.M.E is a development and deployment framework focused on game scenarios, while D.A.T.A is a data framework for general 'chain-like' scenarios, mainly solving the enhancement of AI Agent data interaction capabilities such as cross-blockchain data processing, privacy computation, and automated decision-making. Here, let's compare the understanding of D.A.T.A with the G.A.M.E framework:

1. The G.A.M.E framework provided by @virtuals_io is an AI agent that helps developers create game scenarios that can independently plan actions and make decisions. Its main service object is the LLMs large model.

Allowing large models to be able to make autonomous decisions and action plans based on natural language input, using a set of fine-tuned High-Level Planner (HLP) and Low-Level Planner (LLP). The HLP formulates strategies and tasks, while the LLP translates tasks into specific executable actions. Ultimately, developers can quickly build and deploy AI agents for production environments based on modular components. For example, providing intelligent decision-making for NPCs or players in games.

In contrast, CARV provides the D.A.T.A framework, which is a "data" infrastructure for general scenarios, with the goal of providing high-quality on-chain and off-chain data support for AI agents. Its main service object is the inter-chain "data" communication and interaction capability of the AI Agent.

As a modular and highly scalable general-purpose public chain, its SVM Chain introduces a cross-chain data standardization protocol, enabling AI Agents to uniformly access and process data from different blockchains. At the same time, the blockchain's verifiable and traceable mechanism ensures the security of data during transmission and processing. In addition, the application of TEE and ZK technologies ensures privacy. It is not difficult to see that CARV mainly defines a mechanism for AI Agents to adapt and interact across chains.

2. How to do it specifically? The CARV ecosystem for adapting AI Agent's cross-chain interaction is mainly divided into four core components: SVM Chain, D.A.T.A framework, CARV_ID, CARV_Labs; see the documentation for reference.

1. SVM Chain provides the underlying infrastructure of the blockchain, including basic functions such as processing cross-chain transactions, supporting the operation of smart contracts, and maintaining the consensus mechanism, which is also the supporting chain infrastructure required for the normal operation of the D.A.T.A framework.

2. D.A.T.A framework and standards, mainly including cross-chain data standardization, data aggregation and parsing, privacy computing support, etc., including obtaining raw data from SVM Chain and associating it through the ID system and Agent identity system, and finally outputting standardized data to the application layer;

3. CARV_ID identity management system, based on the ERC7231 standard implementation, mainly includes AI Agent's identity marking, identity verification, permission management, data authorization, etc., mainly working in collaboration with the D.A.T.A framework system for data management.

4, CARV_Labs, mainly through project incubation, ecological application landing, support for technological innovation, etc., provides basic support for the landing of AI Agent applications, and ultimately enables AI Agent applications supported by other technology framework modules to truly land.

In summary, it is clear that CARV takes advantage of its inherent advantages of a chain structure in entering the AI Agent track. It seizes the "function point" of processing on-chain and off-chain data required for the normal operation of AI Agents, aggregates data, defines data standards, and builds data validation and traceability mechanisms, thereby making CARV a blockchain architecture that can run AI Agents.

There are fundamental differences between the G.A.M.E and D.A.T.A frameworks. One focuses on the autonomous decision-making and action execution capabilities of AI Agents in exploring game scenarios, enabling AI Agents to efficiently understand natural language inputs and convert them into actions within the game. The other spans across multiple chain environments, aiming to meet the chain-driven needs through AI Agent, with 'data' as the entry point, making CARV a universal infrastructure chain that serves AI Agents first.