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  "text": "This paper is worth reading carefully.\n\nIt introduces System 3 for AI Agents.\n\nThe default approach to LLM agents today relies on System 1 for fast perception and System 2 for deliberate reasoning.\n\nBut they remain static after deployment. No self-improvement. No identity continuity. No intrinsic motivation to learn beyond assigned tasks.\n\nThis 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.\n\nArtificial life requires four psychological foundations mapped to computational modules:\n\n- Meta-cognition monitors and audits ongoing reasoning.\n- Theory-of-mind models users' beliefs and intentions.\n- Intrinsic motivation drives curiosity-based exploration.\n- Episodic memory maintains autobiographical context across sessions.\n\nHere is how it works:\n\n> Process-Supervised Thought Search captures and validates reasoning traces.\n\n> A Memory Module maintains a structured graph of goals and experiences.\n\n> Self and User Models track capabilities and beliefs.\n\n> A Hybrid Reward Module blends external task feedback with intrinsic signals like curiosity and mastery.\n\nIn a 36-hour continuous deployment, Sophia demonstrated persistent autonomy.\n\nDuring 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.\n\nThis moves agents from transient problem-solvers to adaptive entities with coherent identity, transparent introspection, and open-ended competency growth.\n\nPaper: https://t.co/Eyy7mI9P1i\n\nLearn to build effective AI agents in our academy: https://t.co/zQXQt0PMbG",
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