Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains
May 24, 2025 Β· Declared Dead Β· π IEEE Symposium on Artificial Life
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Botao Amber Hu, Helena Rong
arXiv ID
2506.04236
Category
cs.MA: Multiagent Systems
Cross-listed
cs.AI,
cs.HC,
cs.NE
Citations
2
Venue
IEEE Symposium on Artificial Life
Last Checked
3 months ago
Abstract
In Artificial Life (ALife) research, replicating Open-Ended Evolution (OEE)-the continuous emergence of novelty observed in biological life-has usually 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-environment system that continually exchanges information or energy with its environment. A recent technological innovation in Decentralized Physical Infrastructure Network (DePIN), which provides permissionless computational substrates, enables the deployment of Large Language Model-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-environment 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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multiagent Systems
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Mean Field Multi-Agent Reinforcement Learning
π
π
The Cartographer
A Survey and Critique of Multiagent Deep Reinforcement Learning
π
π
The Cartographer
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
π
π
The Cartographer
Collaborative vehicle routing: a survey
R.I.P.
π»
Ghosted
Deep Reinforcement Learning for Swarm Systems
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted