# Modeling Conceptual Understanding in Image Reference Games

arXiv: https://arxiv.org/abs/1910.04872 · HTML: https://mlsanity.com/paper/1910.04872/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Learning to Mediate Disparities Towards Pragmatic Communication](https://mlsanity.com/paper/2203.13685/) (2022)
- [Towards More Human-like AI Communication: A Review of Emergent Communication Research](https://mlsanity.com/paper/2308.02541/) (2023)
- [Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind](https://mlsanity.com/paper/2305.19933/) (2023)
- [Personalizing MLLMs via Reinforced Multimodal Reference Game](https://mlsanity.com/paper/2606.28845/) (2026)

## Abstract (arXiv)

An agent who interacts with a wide population of other agents needs to be aware that there may be variations in their understanding of the world. Furthermore, the machinery which they use to perceive may be inherently different, as is the case between humans and machines. In this work, we present both an image reference game between a speaker and a population of listeners where reasoning about the concepts other agents can comprehend is necessary and a model formulation with this capability. We focus on reasoning about the conceptual understanding of others, as well as adapting to novel gameplay partners and dealing with differences in perceptual machinery. Our experiments on three benchmark image/attribute datasets suggest that our learner indeed encodes information directly pertaining to the understanding of other agents, and that leveraging this information is crucial for maximizing gameplay performance.