# Compositional Understanding in Signaling Games

arXiv: https://arxiv.org/abs/2507.15706 · HTML: https://mlsanity.com/paper/2507.15706/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 36 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [The emergence of numerical representations in communicating artificial agents](https://mlsanity.com/paper/2602.10996/) (2026)

## Abstract (arXiv)

Receivers in standard signaling game models struggle with learning compositional information. Even when the signalers send compositional messages, the receivers do not interpret them compositionally. When information from one message component is lost or forgotten, the information from other components is also erased. In this paper I construct signaling game models in which genuine compositional understanding evolves. I present two new models: a minimalist receiver who only learns from the atomic messages of a signal, and a generalist receiver who learns from all of the available information. These models are in many ways simpler than previous alternatives, and allow the receivers to learn from the atomic components of messages.