A Motivational Architecture for Conversational AGI

2026-06-03

AI Summary

Traditional motivational architectures struggle to accommodate dialogic AGI systems in which language forms the perception–action loop, user psychology constitutes the environment, and speech acts represent outcomes. This work proposes a novel motivational architecture tailored for dialogic agents, reconceptualizing homeostatic regulation along dimensions such as competence maintenance, uncertainty reduction, and affiliative drive within linguistic interaction. The framework features a ten-stage motivational processing pipeline that integrates dual decision strategies—urgent response handling and multi-objective optimization—and incorporates a functional affect model distinguishing pre-action appraisals from post-action emotions. Built upon the OpenPsi motivational spectrum and MetaMo’s high-level scaffolding, it employs a modular execution architecture to decouple cognitive regulation from situational appraisal. Implemented successfully in CompanionAgent and ResearchAgent, this approach offers a scalable, dialogue-native motivational mechanism for social robotics and general-purpose AGI.

Abstract

Motivational architectures in cognitive AI have largely been designed for physical agents regulating bodily needs. Conversational agents operate in a different regime: their sensorimotor loop is linguistic, their environment is a user's evolving mental state, and their consequential actions are speech acts, tool invocations, and strategic silences. This paper proposes a conversational reinterpretation of the OpenPsi motivational lineage, coupled to MetaMo's higher-level motivational scaffold, for agents built on a modular execution substrate. Homeostasis is recast in dialogue-native terms: the agent regulates competence, uncertainty reduction, affiliation, affinity, legitimacy, nurturing, and aesthetic coherence rather than bodily deficits. We propose three contributions: a ten-stage motivational processing pipeline that architecturally separates cognitive modulation from situational appraisal; a dual decision strategy blending urgency-driven fast response with deliberative multi-goal optimization; and an architecturally useful distinction between pre-action feelings and post-action emotions as functionally different forms of affect. We specialize the framework to two example agents -- CompanionAgent and ResearchAgent -- and sketch its extension to social robotics and domain-generic human-level AGI.

Problem

• motivational architecture
• conversational AGI
• dialogue-native homeostasis
• affect regulation
• cognitive AI

Innovation

• motivational architecture
• conversational AGI
• cognitive modulation
• dual decision strategy
• affective computing