A Motivational Architecture for Conversational AGI

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

This paper proposes a novel motivational architecture for conversational Artificial General Intelligence (AGI), reinterpreting the OpenPsi lineage and integrating it with MetaMo's formal scaffold for agents built on a modular execution substrate. Unlike traditional cognitive AI for physical agents, this framework recasts homeostasis in dialogue-native terms, regulating needs such as competence, uncertainty reduction, affiliation, and legitimacy. The architecture introduces three key contributions: a ten-stage motivational processing pipeline that separates cognitive modulation from situational appraisal, a dual decision strategy blending fast urgency-driven responses with deliberative multi-goal optimization, and a functional distinction between pre-action feelings and post-action emotions. The framework is specialized for CompanionAgent and ResearchAgent, demonstrating its applicability to diverse conversational AI systems.

Key takeaway

For AI Architects designing robust conversational AGI, you should prioritize implementing explicit motivational architectures rather than relying solely on implicit prompt-based methods. This approach enables your agents to balance multiple drives, maintain self-coherence, and adaptively respond to shifting contexts without destabilizing. By adopting a structured motivational layer, you can ensure inspectable control structures and move beyond pseudo-affect, supporting long-term memory, tool use, and social intelligence.

Key insights

Conversational AGI requires a motivational architecture regulating dialogue-native needs through a structured, inspectable processing pipeline.

Principles

• Dialogue-native needs drive conversational AGI homeostasis.
• Separate cognitive modulation from situational appraisal.
• Distinguish pre-action feelings from post-action emotions.

Method

A ten-stage pipeline: perceive, estimate needs, modulate cognition, construct feelings, appraise situation, generate/score candidates, execute action, evaluate outcome, and blend with governor.

In practice

• Implement explicit world, self, and user models.
• Utilize abstract, typed actions for agent legibility.
• Develop agents with specific goal priors and need weights.

Topics

• Motivational Architecture
• Conversational AGI
• OpenPsi
• MetaMo
• Cognitive Modulation
• Dual-Process Decision

Best for: AI Scientist, AI Architect, Research Scientist

Related on AIssential

• A Motivational Architecture for Conversational AGI — A new motivational architecture redefines homeostasis for conversational AGI agents, regulating dialogue-native drives like competence and affiliation.
• The Brain of the Machine: Mastering Cognitive Architectures in Agentic AI — Cognitive architectures, not just context, drive intelligence in 2026 agentic AI applications.
• APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI — APEX-MEM enhances long-term conversational AI through structured memory, temporal grounding, and intelligent retrieval.
• The Secret is Out: How we're Building AGI — True AGI requires conceptual knowledge validation and integrated neuro-symbolic architecture, unlike statistical LLMs.
• [R] The Spark Architecture: Defining a Motivation-Driven Cognitive Loop for AGI — The Spark Architecture proposes a motivation-driven meta-logic layer to enable continuous self-interrogation and skill acquisition in AGI.
• Structured Intent as a Protocol-Like Communication Layer: Cross-Model Robustness, Framework Comparison, and the Weak-Model Compensation Effect — Structured intent representations significantly enhance goal alignment and reduce variance across diverse AI models and languages.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.