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arXiv · 2509.23757

Transparent Visual Reasoning via Object-Centric Agent Collaboration

Benjamin Teoh·Ben Glocker·Francesca Toni·Avinash Kori
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Abstract

A central challenge in explainable AI, particularly in the visual domain, is producing explanations grounded in human-understandable concepts. To tackle this, we introduce OCEAN (Object-Centric Explananda via Agent Negotiation), a novel, inherently interpretable framework built on object-centric representations and a transparent multi-agent reasoning process. The game-theoretic reasoning process drives agents to agree on coherent and discriminative evidence, resulting in a faithful and interpretable decision-making process. We train OCEAN end-to-end and benchmark it against standard visual classifiers and popular posthoc explanation tools like GradCAM and LIME across two diagnostic multi-object datasets. Our results demonstrate competitive performance with respect to state-of-the-art black-box models with a faithful reasoning process, which was reflected by our user study, where participants consistently rated OCEAN's explanations as more intuitive and trustworthy.

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Benjamin Teoh, Ben Glocker, Francesca Toni, Avinash Kori. 2025-09-28. Transparent Visual Reasoning via Object-Centric Agent Collaboration. https://arxiv.org/abs/2509.23757

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