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This paper introduces the Mirror Agent Model, a Bayesian architecture designed to make AI agent behavior and decision-making processes interpretable through explicit observer modeling. By integrating off-the-shelf saliency methods, the framework aims to produce legible explanations for agent intentions, addressing critical transparency needs in complex autonomous systems. The work represents a significant step toward trustworthy AI by formalizing how agents communicate their internal states to external observers.
Surse care acoperăarXiv, LessWrong
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The Mirror Agent Model: a Bayesian Architecture for Interpretable Agent Behavior

arXiv1 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.05190v1 Announce Type: new Abstract: In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer to this architecture as the Mirror Agent Model because it defines the observer model, that is the target of explicit and implicit communications, as a mirror of the agent's. With the goal of providing a general understanding of this work, we firstly show prior relevant results addressing the informative communication of agents intentions and the production of legible behavior. In the second part of the paper we furnish the architecture with novel capabilities for explanations through off-the-shelf saliency methods, followed by preliminary qualitative results.

Abstract:In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer to this architecture as the Mirror Agent Model because it defines the observer model, that is the target of explicit and implicit communications, as a mirror of the agent's. With the goal of providing a general understanding of this work, we firstly show prior relevant results addressing the informative communication of agents intentions and the production of legible behavior. In the second part of the paper we furnish the architecture with novel capabilities for explanations through off-the-shelf saliency methods, followed by preliminary qualitative results.

Artificial Intelligence (cs.AI)

arXiv:2609.05190 [cs.AI]

(or arXiv:2609.05190v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.05190 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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