This paper is worth reading carefully.

It introduces System 3 for AI Agents.

The default approach to LLM agents today relies on System 1 for fast perception and System 2 for deliberate reasoning.

But they remain static after deployment. No self-improvement. No identity continuity. No intrinsic motivation to learn beyond assigned tasks.

This new research introduces Sophia, a persistent agent framework built on a proposed System 3: a meta-cognitive layer that maintains narrative identity, generates its own goals, and enables lifelong adaptation.

Artificial life requires four psychological foundations mapped to computational modules:

- Meta-cognition monitors and audits ongoing reasoning.

- Theory-of-mind models users' beliefs and intentions.

- Intrinsic motivation drives curiosity-based exploration.

- Episodic memory maintains autobiographical context across sessions.

Here is how it works:

> Process-Supervised Thought Search captures and validates reasoning traces.

> A Memory Module maintains a structured graph of goals and experiences.

> Self and User Models track capabilities and beliefs.

> A Hybrid Reward Module blends external task feedback with intrinsic signals like curiosity and mastery.

In a 36-hour continuous deployment, Sophia demonstrated persistent autonomy.

During user idle periods, the agent shifted entirely to self-generated tasks. Success rate on hard tasks jumped from 20% to 60% through autonomous self-improvement. Reasoning steps for recurring problems dropped 80% through episodic memory retrieval.

This moves agents from transient problem-solvers to adaptive entities with coherent identity, transparent introspection, and open-ended competency growth.

Paper: arxiv.org/abs/2512.18202

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