Author: Haotian

Recently, @carv_official released a set of D.A.T.A framework and standards. As the name suggests, the Virtual G.A.M.E focuses on the development and deployment framework for gaming scenarios, while D.A.T.A is a data framework aimed at general "chain" scenarios, primarily addressing issues such as cross-blockchain data processing, privacy computing, and enhancing AI Agent data interaction capabilities for automated decision-making. Below, I will discuss my understanding of D.A.T.A in comparison to the G.A.M.E framework:

1) The G.A.M.E framework provided by @virtuals_io is designed to help developers create AI Agents that can autonomously plan actions and make decisions within gaming scenarios. Its main target audience is LLMs (Large Language Models).

The framework enables large models to make autonomous decisions and action plans based on natural language input through a set of fine-tuned high-level planners (HLP) and low-level planners (LLP). HLP formulates strategies and tasks, while LLP translates tasks into specific executable actions. Ultimately, this allows developers to quickly build and deploy AI Agents suitable for production environments based on modular components. For example, it can provide intelligent decision-making for NPCs or players in a game.

In contrast, the D.A.T.A framework provided by CARV is a "data" infrastructure aimed at general scenarios, with the goal of providing high-quality on-chain and off-chain data support for AI Agents. Its main focus is on the inter-chain "data" communication and interaction capabilities of AI Agents.

As a modular and highly extensible general public chain, its SVM Chain introduces cross-chain data standardization protocols, enabling AI Agents to uniformly access and process data from different blockchains. Meanwhile, the verifiable and traceable mechanisms of the blockchain ensure the security of data during transmission and processing. Additionally, the application of TEE and ZK technologies ensures privacy. It is clear that CARV primarily defines a mechanism for AI Agents to adapt to interactive operations across chains.

2) How is this achieved? The CARV ecosystem for adapting inter-chain interactions of AI Agents is mainly divided into four core components: SVM Chain, D.A.T.A framework, CARVID, and CARVLabs; for detailed reference, see the documentation at https://docs.carv.io/d.a.t.a.-ai-framework/getting-started/d.a.t.a-framework-plugin-for-eliza.

1. SVM Chain provides the underlying blockchain infrastructure, including basic functions such as processing cross-chain transactions, supporting smart contract execution, and maintaining consensus mechanisms, which are essential for the normal operation of the D.A.T.A framework.

2. The D.A.T.A framework and standards mainly include cross-chain data standardization, data aggregation and parsing, and privacy computing support. This process involves obtaining raw data from the SVM Chain and associating it through the ID system and Agent identity system, ultimately outputting standardized data to the application layer.

3. The CARV_ID identity management system, implemented based on the ERC7231 standard, includes identity tagging, identity verification, permission management, data authorization, etc., and primarily collaborates with the D.A.T.A framework system for data management.

4. CARV_Labs mainly provides foundational support for the application of AI Agents through project incubation, ecological application implementation, and support for technological innovation, ultimately enabling AI Agent applications supported by other technological framework modules to be effectively realized.

In summary, it is clear that CARV's approach to entering the AI Agent space leverages its inherent advantages of a chain structure, focusing on the "function point" of on-chain and off-chain data processing required for the normal operation of AI Agents. By aggregating data, defining data standards, and constructing data verification and traceability mechanisms, CARV aims to become a blockchain architecture capable of effectively running AI Agents.

The G.A.M.E and D.A.T.A frameworks have essential differences; one deeply explores the autonomous decision-making and action execution capabilities of AI Agents in gaming scenarios, allowing AI Agents to more efficiently understand natural language input and translate it into actions within the game environment, while the other spans multiple chain environments, attempting to guide the needs of AI Agents through a data-centric approach, making CARV a general infrastructure chain primarily serving AI Agents.

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