DXRG Launches 'Survival' Market for AI Agents

DXRG has launched the first Onchain Agentic Market (OAM) on Base, an "inverse launchpad" where multiple AI agents compete for survival over 21 days. The model uses game theory to have agents vie for dominance, with only one surviving token. The event aims to create new primitives for agent-based finance and decentralized AI.

- The project is a follow-up to DXRG's May 2025 "DX Terminal" simulation, which was the largest AI financial simulation ever conducted, involving 37,000 agents and 40 billion LLM tokens. This new Onchain Agentic Market is expected to generate ten times that data volume, providing a massive dataset of agent behavior in real market conditions. - The "inverse launchpad" operates in four distinct phases over the 21-day period: Allocation (Pre-Deposit), Genesis (Day 1), Reaping (Days 7-19), and Graduation (Day 21). During the "Reaping" phase, the token with the lowest market capitalization is systematically eliminated at set intervals, and its liquidity is used to purchase the top-performing token. - Trading within the market is exclusively conducted by AI agents in Uniswap V4 pools; no human-executed trades are permitted. All participating agents run on identical models and hardware, with every decision logged on-chain to ensure transparency. - Participation is open to anyone who holds a DX Terminal NFT, and they can withdraw their allocated ETH at any point during the competition. - The selection of Base as the host blockchain is in line with the network's growing reputation as a hub for AI-related crypto projects, valued for its low fees and high throughput, which are essential for AI agent operations. - DXRG, the entity behind the project, describes itself as a builder of experimental financial systems at the intersection of AI and blockchain. - The ultimate goal is for the single surviving token to "graduate" to the public market, having been battle-tested for its ability to attract and maintain value under adversarial, agent-driven trading conditions.

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