VUB and UNamur study shows how AI agents develop their own ‘artificial natural language’ to communicate about complex data

BRUSSELS – XX October 2026 – Researchers from the VUB’s Artificial Intelligence Lab and the University of Namur (UNamur) demonstrate, in a new large-scale study, how autonomous AI systems can spontaneously develop, without human intervention, a functional and shared language. Unlike large language models such as ChatGPT, which learn existing human language by heart, these AI agents construct their own communication system, directly rooted in their specific sensors and environment. This discovery represents a major breakthrough for communication between autonomous systems, such as self-driving cars, robots and industrial sensors.

A layer of abstraction for non-human senses

Current AI language models focus on understanding and generating human languages such as Dutch or English. These human languages are historically rooted in our own sensory perceptions. When physical or virtual AI agents are equipped with sensors for which humans have no direct sensory equivalent – such as lidar, sonar, infrared or chemical analysis – existing human language proves insufficient.

Jérôme Botoko Ekila, a PhD student at the VUB’s AI Lab, explains: ‘Instead of forcing AI systems to communicate in a human language that does not correspond to their sensors, we allow them to develop their own “artificial natural language”. They construct effective concepts that correspond exactly to what they perceive via their sensors, even if the data is incomprehensible to humans.’

As, in practice, the sensors on different machines or vehicles can never be calibrated identically, the researchers believe that this language offers the ideal solution.

Paul Van Eecke, professor of artificial intelligence at the VUB: “In industrial or physical applications, it is simply unrealistic to expect all the sensors on different robots or vehicles to be calibrated in exactly the same way. Humans solve this problem through language: if I say I am standing near the ‘red car’, we understand each other without our eyes registering exactly the same colour values. Language provides a layer of abstraction above the sensor readings, and this is precisely what we are now allowing these AI agents to construct autonomously.”

Self-organisation and evolution

Instead of pre-programmed words or fixed codes, the researchers simply provided the agents with the basic mechanisms underpinning the evolution of human language: the local invention of words, the adaptation of conceptual prototypes, and the incorporation of feedback following successful or failed communication.

Katrien Beuls, a professor at UNamur, explains: “What’s fascinating is that there’s no central computer imposing the rules. Each time, we let two agents communicate with one another at a local level. It is solely through these individual interactions and updates that a global convention emerges spontaneously across the entire population. This principle of self-organisation allows the language to optimise itself automatically: the agents create exactly the number of categories needed to carry out their tasks efficiently, without superfluous details.”

Tested on 37 datasets

The universal applicability of the method has been demonstrated across 37 varied and complex datasets. The agents successfully developed their own vocabulary to communicate, notably regarding physico-chemical analyses of wine, NASA data on exoplanets, visual datasets, and images of birds or faces. In all these situations, the agents successfully synthesised the data into new, shared concepts.

Transparent and adaptive

Transparency is a notable advantage of the systems developed. Unlike many ‘black-box’ AI models, researchers can precisely determine the meaning of a specifically developed word, as the prototype can be directly linked to the values of the underlying sensors.

Furthermore, these evolving linguistic systems are extremely robust. When the environment changes, when sensors produce noise, or when individual agents have a different structure, the language automatically adapts to the new circumstances through continuous interaction.

Reference

Botoko Ekila, J., Verheyen, L., Nevens, J., Beuls, K., & Van Eecke, P. (2026). Emergent Communication in Continuous Worlds: Self-Organisation of Conceptually Grounded Vocabularies at Scale. Transactions of the Association for Computational Linguistics, vol. 14, pp. 2234–2257. DOI: 10.1162/TACL.a.799

Funding and collaboration

This research was carried out as part of a collaboration between the Artificial Intelligence Laboratory at the Vrije Universiteit Brussel (VUB) and the Faculty of Computer Science at the University of Namur (UNamur), and was funded by the Research Foundation – Flanders (FWO) and the Belgian National Research Fund (FNRS) as part of the WEAVE project.

Contact

(telephone numbers available from the editorial team)

Koen Stein

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