Recursive Self-Improving Agent World-Models
An Agent World-Model cartridge is epistemically typed intelligence substrate for AI agents. Its primary function is belief-state initialization - supplying the domain, causal structure, confidence architecture, reasoning rules & boundaries of legitimate inference. World model cartridge contain not only knowledge, like most prompts, but governance: it tells the model how strongly to believe claims, how not to reason, what distinctions to preserve.
It controls:
- Attention — what the agent notices.
- Ontology — what kinds of things the agent believes exist.
- Inference — which causal moves are legitimate.
- Uncertainty — what the agent may claim confidently.
- Retrieval — where it looks for supporting detail.
- Narrative resistance — which seductive stories it must reject.
- Simulation — how it predicts the system will behave.
This is the state representation and epistemic constitution around which a recursive self-improving world model system can be built. The crucial phrase is external evidence. If the LLM recursively rewrites its own synthesis without new evidence, it does not improve. The most powerful form: prediction and reconciliation. Eventually the system can recursively improve not just its beliefs, but its method of belief formation.