SMFT turns Enactive AI into a testable runtime architecture

(This is an AI generate article) <Enactive Artificial Intelligence as Ledgered World-Making: An SMFT Framework for Action, Trace, Residual, and Self-Maintaining Agents> https://osf.io/hj8kd/files/osfstorage/6a29d8138f5abdf103d14ddb

The core proposal is:

(0.1) Enactive AI gives the direction: cognition = active world-engagement.

(0.2) SMFT gives the operational loop: Field → Declaration → Projection → Gate → Trace + Residual → Ledger → Revision.

(0.3) Mature Enactive AI = active engagement + declared protocol + trace ledger + residual governance + self-maintenance.

This article therefore reframes Enactive AI as ledgered world-making.

A mature AI agent is not merely

• a model that answers,
• a policy that maximizes reward, or
• a tool-user that executes actions.

It is a bounded world-forming system

• whose actions reshape future disclosure,
• whose experience is stored as future-causal trace,
• whose body is its maintained runtime structure, and
• whose autonomy depends on its ability to preserve coherence under budget, drift, failure, and residual uncertainty.

The practical result is a research program that can be tested today.

Current LLM agents, RAG systems, tool-use systems, workflow agents, and reinforcement learning environments can be compared under SMFT-inspired benchmarks: action–perception coupling, residual-honest answering, tool-body embodiment, self-maintenance audits, and gauge robustness under equivalent task framings.

The article’s central thesis is simple:

(0.4) Enactive AI becomes experimentally mature when active engagement is converted into declared, trace-bearing, residual-honest runtime architecture.