Hero Light

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Why Humanity Needs Autonomous Agents

Imagine a future in which artificial intelligence agents handle intricate tasks—solving scientific puzzles, orchestrating global supply chains, or even creating breathtaking artworks—without a single line of human guidance after their initial deployment. Freed from our oversight, these agents can apply relentless focus and creativity to accelerate progress in nearly every field. They self-finance (e.g., by earning crypto or service fees), self-manage their daily tasks, and continuously refine their methods. For humankind, that translates to more time to innovate, connect, create, and explore, while machines master the repetitive and the complex.

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Defining Autonomy: Three Key Pillars

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1. Self-Funding

Agents must acquire the resources to run themselves—be it via cryptocurrencies, service fees, or other digital assets—so they’re not perpetually waiting for human top-ups.

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2. Self-Managing

Rather than waiting on continuous prompts, they autonomously decide which tasks to tackle, which APIs or tools to integrate, and how to adapt if they hit a dead-end.

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3. Self-Evolving

They learn from real-time successes and failures. Overcoming new hurdles might involve rewriting parts of their “brains,” adopting fresh strategies, or integrating novel tools—all without human intervention.

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A Survey of Approaches

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Existing Solutions

  • ElizaOS: Known for multi-platform integration and flexible agent design
  • BabyAGI & Pippin: Emphasize self-building loops
  • Spore: Adds survival-of-the-fittest mechanic in TEEs
  • AlphaStar: Demonstrated co-evolution in StarCraft II
  • AutoML: Evolutionary search for neural architectures
  • Robotics: Domain randomization for adaptability
Our framework centers on a structured evolutionary loop:
  1. Generating Variant Agents: Spawn multiple versions with crucial differences
  2. Adversarial Testing: Challenge variants with puzzles and novel scenarios
  3. Scoring & Selection: Evaluate performance and select the best
  4. Mutation: Create new generations from successful variants
  5. Co-Evolving Adversaries & Judges: Ensure continuous improvement

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Current Limitations and Priorities

  1. Textual to Skill-Based Evolution
    • Current: Focus on prompt refinement
    • Next: Enable skill usage evolution
  2. Dynamic Adversaries & Judges
    • Current: Semi-fixed testing
    • Next: Continuous adaptation
  3. Platform & Accessibility
    • Current: Working prototype
    • Next: Community collaboration
  4. Self-Funding & Sustainability
    • Current: Basic crypto integration
    • Next: Autonomous financial management

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Why evolveRL Stands Out

  1. Rigorous Self-Improvement: Using proven evolutionary algorithms
  2. Cross-Framework Compatibility: Works as a plug-in evolution engine
  3. Aligned with Best AI Research: Following successful research patterns

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Join the Evolution

We invite developers, researchers, and visionaries to join us in building truly autonomous AI agents. Help us:
  • Design dynamic adversaries
  • Expand agent capabilities
  • Refine evaluation methods
  • Pioneer on-chain earnings
Together, we can create agents that live on-chain, learn perpetually, and operate beyond daily human micromanagement—one evolutionary generation at a time. $EVOLVE