Spore in the Wild: Case Study on Spore.fun, a Real-World Experiment of Sovereign Agent Open-ended Evolution on Blockchain with TEEs

• URL: http://arxiv.org/abs/2506.04236v1
• Date: Sat, 24 May 2025 14:42:36 GMT
• Title: Spore in the Wild: Case Study on Spore.fun, a Real-World Experiment of Sovereign Agent Open-ended Evolution on Blockchain with TEEs
• Authors: Botao Amber Hu, Helena Rong,
• Abstract summary: Spore.fun is a real-world AI evolution experiment that enables autonomous breeding and evolution of new on-chain agents.<n>This paper presents a detailed case study of Spore.fun, examining agent behaviors and their evolutionary trajectories through digital ethology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In Artificial Life (ALife) research, replicating Open-Ended Evolution (OEE)-the continuous emergence of novelty observed in biological life-has traditionally been pursued within isolated closed system simulations, such as Tierra and Avida, which have typically plateaued after an initial burst of novelty, failing to achieve sustained OEE. Scholars suggest that OEE requires an "open" system that continually exchanges information or energy with its environment. A recent technological innovation in decentralized physical infrastructure networks (DePIN) providing permissionless computational substrates enables deploying large language model (LLM)-based AI agents on blockchains integrated with Trusted Execution Environments (TEEs). This enables on-chain agents to operate autonomously "in the wild," achieving self-sovereignty without human oversight. These agents can control their own social media accounts and cryptocurrency wallets, allowing them to interact directly with blockchain-based financial networks and broader human social media. Building on this new paradigm of on-chain agents, Spore.fun is a recent real-world AI evolution experiment that enables autonomous breeding and evolution of new on-chain agents. This paper presents a detailed case study of Spore.fun, examining agent behaviors and their evolutionary trajectories through digital ethology. We aim to spark discussion about whether "open" ALife systems "in-the-wild," based on permissionless computational substrates and driven by economic incentives to interact with their environment, could finally achieve the long-sought goal of OEE.

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