CARV releases D.A.T.A framework: Creating a Cross-Chain Interaction data infrastructure for AI Agents

Author: Haotian

Recently, CARV 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 addressing enhanced AI Agent data interaction capabilities for cross-blockchain data processing, privacy computation, and automated decision-making.

Let’s talk about the understanding of D.A.T.A in comparison to the G.A.M.E framework:

1. The G.A.M.E framework provided by Virtual is an AI Agent that helps developers create game scenes that can autonomously plan actions and make decisions. Its main service target is LLMs large models.

Allow large models to make autonomous decisions and action plans based on natural language input, through a set of fine-tuned High-Level Planner (HLP) and Low-Level Planner (LLP). HLP formulates strategies and tasks, while 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 decisions for NPCs or players in games.

In contrast, the CARV provides a D.A.T.A framework, which is a general-purpose ‘data’ infrastructure that aims to provide high-quality on-chain and off-chain data support for AI Agents.