arXiv:2606.05411v1 Announce Type: new 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. Hom
The paper leverages existing foundational motivational architectures (OpenPsi) and higher-level scaffolds (MetaMo) to address a critical gap in conversational AI research for AGI, indicating a maturing field ready for integration of disparate components.
This research provides a theoretical and architectural framework for developing more sophisticated, context-aware, and goal-directed conversational AI agents, moving beyond purely reactive or task-specific systems.
The focus shifts from simple conversational interfaces to agents with intrinsic motivations, linguistic sensorimotor loops, and the ability to strategically influence user mental states through speech acts.
- · AI research institutions
- · Conversational AI developers
- · High-value white-collar industries
- · Companies relying on simple rule-based chatbots
- · Human agents performing routine cognitive tasks
Immediate advancement in the theoretical understanding and practical implementation of AGI for conversational systems.
Pervasive deployment of highly autonomous and motivational AI agents capable of complex linguistic interaction and workflow orchestration.
Reconfiguration of informational workforces, with AI agents handling a vast array of cognitive tasks and creative synthesis.
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Read at arXiv cs.AI