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.

翻译：认知AI中的动机架构主要被设计用于调节身体需求的物理智能体。对话智能体运行于不同模式：其感知运动回路是语言性的，其环境是用户不断演变的心智状态，其重要行为是言语行为、工具调用和策略性沉默。本文提出对OpenPsi动机谱系的对话式重新解释，结合MetaMo的高级动机支架，适用于构建在模块化执行基底上的智能体。内稳态被重新定义为对话原生的术语：智能体调节的是能力、不确定性降低、亲和性、情感联系、合法性、滋养和审美连贯性，而非身体缺陷。我们提出三项贡献：一个十阶段动机处理流水线，在架构上将认知调节与情境评估分离；一种双重决策策略，融合紧迫驱动的快速响应与深思熟虑的多目标优化；以及一个在架构上有用的区分——行动前情感与行动后情绪作为功能上不同的情感形式。我们将该框架特化为两个示例智能体——陪伴型智能体与研究型智能体——并概述其向社交机器人和领域通用的人类级通用人工智能的扩展。