aiqbench analysis
This paper proposes Behaviora, a conceptual architecture designed to standardize the representation of robot and agent behaviors through addressable identifiers known as IoB Addresses. By separating what an agent does (Behavior Episodes) from how it expresses itself (Style Profiles) and its internal state (Experience Profiles), the framework aims to create a unified language for both external actions and internal cognitive processes like inner speech. While currently limited to a primitive model without full sensing capabilities, this approach lays foundational groundwork for interoperable multi-agent systems and complex robotic task execution.
Behaviora - A Conceptual Architecture for External and Internal Behavior of Robots and Agents
arXiv:2609.27536v1 Announce Type: cross Abstract: Behaviora is a preliminary conceptual architecture for representing agent and robot behavior, external and internal alike, in an addressable form. A behaving robot or agent performs a Behavior Episode composed of episode components, which can be derived from behavior taxonomies (BTax) and assigned persistent identifiers. We denote these identifiers as IoB (Internet of Behaviors) Addresses. A Behavior Episode specifies what the system does, while a Style Profile (SP) specifies how this behavior is expressed. Style can communicate characteristics of the actor and qualities such as competence and cultural manners. An Experience Profile (EP) represents behaviorally relevant internal state that modulates the execution of an Episode. Finally, a Behavior Compiler maps these behavioral representations to platform-specific actions. We use a primitive touching arm model to show these components and their relations. External Behavior is a result of addressable movements and their styles. Internal Behavior is represented through the same episodic principle and can be rendered as inner speech. Sensing, perception and complex task contexts have not been included in the present implementation, although a conceptual place is reserved for them.
Abstract:Behaviora is a preliminary conceptual architecture for representing agent and robot behavior, external and internal alike, in an addressable form. A behaving robot or agent performs a Behavior Episode composed of episode components, which can be derived from behavior taxonomies (BTax) and assigned persistent identifiers. We denote these identifiers as IoB (Internet of Behaviors) Addresses. A Behavior Episode specifies what the system does, while a Style Profile (SP) specifies how this behavior is expressed. Style can communicate characteristics of the actor and qualities such as competence and cultural manners. An Experience Profile (EP) represents behaviorally relevant internal state that modulates the execution of an Episode. Finally, a Behavior Compiler maps these behavioral representations to platform-specific actions. We use a primitive touching arm model to show these components and their relations. External Behavior is a result of addressable movements and their styles. Internal Behavior is represented through the same episodic principle and can be rendered as inner speech. Sensing, perception and complex task contexts have not been included in the present implementation, although a conceptual place is reserved for them.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2609.27536 [cs.RO]
(or arXiv:2609.27536v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.27536 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 275 words
Related coverage

[AINews] not much happened today
Latent Space · Sep 18, 2026

[AINews] not much happened today
Latent Space · Sep 10, 2026

[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)
Latent Space · Sep 17, 2026

News
Quantum Computing Report · Jun 7, 2026