mlsanityData to 14 Oct 2026

Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains

arXiv 2506.04236 ↗First posted 24 May 2025cs.MA cs.AI cs.HCcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 15 of its 56 references that are in the map. First posted on arXiv 24 May 2025.

Builds on

15 of 56 references.

References · 15

  1. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  2. 2025Multi-Agent Risks from Advanced AIcited by 163
  3. 2020Liquid Time-constant Networkscited by 161
  4. 2020AI Research Considerations for Human Existential Safety (ARCHES)cited by 33
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  6. 2021Quantifying Blockchain Extractable Value: How dark is the forest?cited by 15
  7. 2022Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)cited by 15
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  10. 2019An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issuecited by 10
  11. 2025Trustless Autonomy: Understanding Motivations, Benefits, and Governance Dilemmas in Self-Sovereign Decentralized AI Agentscited by 10
  12. 2023A Survey of Secure Computation Using Trusted Execution Environmentscited by 8
3 more
  1. 2025The Philosophic Turn for AI Agents: Replacing centralized digital rhetoric with decentralized truth-seekingcited by 5
  2. 2022Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Researchcited by 3
  3. 2024Privacy-Preserving Decentralized AI with Confidential Computingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026OpenLife: Toward Open-World Artificial Life with Autonomous LLM Agents
  2. 2026Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries
  3. 2026The Agent Economy: A Blockchain-Based Foundation for Autonomous AI Agents
  4. 2026OpenKedge: Governing Agentic Mutation with Execution-Bound Safety and Evidence Chains
  5. 2024JaxLife: An Open-Ended Agentic Simulator
  6. 2026Tapes Together Strong: The Co-evolution of Computation and Cooperation
  7. 2026BloClaw: An Omniscient, Multi-Modal Agentic Workspace for Next-Generation Scientific Discovery
  8. 2026OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution