# EGG: a toolkit for research on Emergence of lanGuage in Games

arXiv: https://arxiv.org/abs/1907.00852 · HTML: https://mlsanity.com/paper/1907.00852/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 30 of 42)

- [How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis](https://mlsanity.com/paper/2402.05863/) (2024)
- [Emergent Communication: Generalization and Overfitting in Lewis Games](https://mlsanity.com/paper/2209.15342/) (2022)
- [Interpretable agent communication from scratch (with a generic visual processor emerging on the side)](https://mlsanity.com/paper/2106.04258/) (2021)
- [A Review of the Applications of Deep Learning-Based Emergent Communication](https://mlsanity.com/paper/2407.03302/) (2024)
- [Cross-Domain Image Captioning with Discriminative Finetuning](https://mlsanity.com/paper/2304.01662/) (2023)
- [Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off](https://mlsanity.com/paper/2301.13083/) (2023)
- [Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability](https://mlsanity.com/paper/2106.03982/) (2021)
- [Referential communication in heterogeneous communities of pre-trained visual deep networks](https://mlsanity.com/paper/2302.08913/) (2023)
- [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026)
- [Emergence of hierarchical reference systems in multi-agent communication](https://mlsanity.com/paper/2203.13176/) (2022)
- [Defending Compositionality in Emergent Languages](https://mlsanity.com/paper/2206.04751/) (2022)
- [NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication](https://mlsanity.com/paper/2407.13999/) (2024)
- [Emergent Quantized Communication](https://mlsanity.com/paper/2211.02412/) (2022)
- [Concept-Best-Matching: Evaluating Compositionality in Emergent Communication](https://mlsanity.com/paper/2403.14705/) (2024)
- [The Curious Case of Representational Alignment: Unravelling Visio-Linguistic Tasks in Emergent Communication](https://mlsanity.com/paper/2407.17960/) (2024)
- [Language Evolution with Deep Learning](https://mlsanity.com/paper/2403.11958/) (2024)
- [Variable-Length Semantic IDs for Recommender Systems](https://mlsanity.com/paper/2602.16375/) (2026)
- [Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel](https://mlsanity.com/paper/2111.02827/) (2021)
- [Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks](https://mlsanity.com/paper/2201.05489/) (2022)
- [Speaking Your Language: Spatial Relationships in Interpretable Emergent Communication](https://mlsanity.com/paper/2406.07277/) (2024)
- [Unsupervised Translation of Emergent Communication](https://mlsanity.com/paper/2502.07552/) (2025)
- [Recommendations for Systematic Research on Emergent Language](https://mlsanity.com/paper/2206.11302/) (2022)
- [ELCC: the Emergent Language Corpus Collection](https://mlsanity.com/paper/2407.04158/) (2024)
- [Semantics and Spatiality of Emergent Communication](https://mlsanity.com/paper/2411.10173/) (2024)
- [NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use](https://mlsanity.com/paper/2509.22479/) (2025)
- [On the Correspondence between Compositionality and Imitation in Emergent Neural Communication](https://mlsanity.com/paper/2305.12941/) (2023)
- [XferBench: a Data-Driven Benchmark for Emergent Language](https://mlsanity.com/paper/2407.03456/) (2024)
- [A Combinatorial Approach to Neural Emergent Communication](https://mlsanity.com/paper/2410.18806/) (2024)
- [A Compressive-Expressive Communication Framework for Compositional Representations](https://mlsanity.com/paper/2501.19182/) (2025)
- [From Grunts to Lexicons: Emergent Language from Cooperative Foraging](https://mlsanity.com/paper/2505.12872/) (2025)

## Abstract (arXiv)

There is renewed interest in simulating language emergence among deep neural agents that communicate to jointly solve a task, spurred by the practical aim to develop language-enabled interactive AIs, as well as by theoretical questions about the evolution of human language. However, optimizing deep architectures connected by a discrete communication channel (such as that in which language emerges) is technically challenging. We introduce EGG, a toolkit that greatly simplifies the implementation of emergent-language communication games. EGG's modular design provides a set of building blocks that the user can combine to create new games, easily navigating the optimization and architecture space. We hope that the tool will lower the technical barrier, and encourage researchers from various backgrounds to do original work in this exciting area.