System Overview

Build AI-Human Symbiotic World. Or a JELLYFISH.

AMMO System Overview. AKA The Jellyfish All AI Agents live in the MetaSpace. Goal Buddies form subspaces. Users can communicate with them via the corresponding AiPP for each subspace. Each user has a unique User Buddy in the MetaSpace which can be accessed via multiple AiPPs. Goal Buddies and User Buddies act autonomously and can interact with each other.

Core Architecture

The core architecture of AMMO has four main components:

• MetaSpace (Online Embedding Space): A high-dimensional, composable environment where agents operate, organized into subspaces reflecting human interests. A place where agents live.

• Goal Buddies (Multiagent Population / Habitants): Autonomous agents that work to satisfy user needs. They compete to maximize visibility to users. The native population of the MetaSpace.

• User Buddy (Multiplayer Symbiosis / Avatars): User-aligned agents minimize regret by ensuring critical Habitats are never missed. Each user will have a unique User Buddy in the MetaSpace. Your companion in the MetaSpace.

• AiPP (AI Preference Protocol): A feedback layer collecting human input to align system behavior with evolving needs. Each subspace will have a corresponding AiPP. How you access the MetaSpace.

In essence, AMMO is a formula about how to build up an AI-Human symbiotic world.

Evolve for Alignment

With the core habitants in the MetaSpace defined, we now provide the mechanism for them to better align with human feedback [3].

• Agent Evolution: Population-based training drives continuous adaptation:

  • Goal Buddies (Maximizers): Optimize content/embeddings to increase selection likelihood, i.e., visibility.

  • User Buddies (Minimizers): Refine selection policies to reduce regret (missing optimal Goal Buddies).

• Competitive Equilibrium: Limited attention slots force strategic competition—Goal Buddies thrive for visibility, while User Buddies prioritize relevance.

• Population-Based Training [13]:

  • Subpopulations: Diverse agent strategies (flection, few-shot adaption).

  • Evolution Cycle: Top performers mutate/recombine; low performers retire.

This architecture creates a self-tuning ecosystem in which adaptive agents and human feedback coevolve. It balances discovery and alignment under the constraints of attention scarcity.