Abstract
We introduce Cognogenics, a new paradigm for artificial life and intelligence that arises not from predefined goals or supervised data, but from an agent’s continuous drive to reduce internal prediction error.
Built upon the Free Energy Principle (FEP), Cognogenics powers Simulation-Integrated Multimodal-Language (SIML) agents—embodied, memory-constrained organisms whose behavior emerges from real-time inference, not external reward signals.
Key Innovations
- No Reward Functions: SIML abandons traditional reinforcement learning and reward-maximization paradigms.
- Bitwise Memory Schema: Agents compress internal models into <1kB active memory using Rust-based memory buffers.
- Lifelong Learning: Agents learn during a single lifespan—not through generations or batch training.
- Emergent Intelligence: Foraging, planning, path formation, and homeostasis arise organically via prediction error minimization.
Why This Matters
Most AI research today is built on borrowed biological metaphors—reinforcement learning (rat mazes), gradient descent (error correction), evolutionary algorithms (genetic mutation). But SIML takes a radical leap: our agents are alive not because we told them how to survive, but because they had to figure it out.
They build internal generative models of the world, predict what comes next, and act to stay within familiar zones of expected surprise. No scripting. No prompts. Just emergence.
About the Author
This research was conducted by Deep SIML Labs, an independent research lab exploring the frontiers of artificial life, recursive cognition, and self-organizing intelligence.
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