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.

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.”

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.

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.

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.

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

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.

Before artificial intelligence can recognize an image, understand a text, or answer a question, it must first be trained. To do this, developers and data scientists use specialized software libraries—true “toolboxes” that enable the construction of these AI models.

A research team comprising the University of Namur, the University of Passau (Germany), and TU Delft (Netherlands) has been studying this software. Their goal: to detect bugs that are particularly difficult to spot and that could cause a program to crash suddenly.

To verify that these libraries are functioning properly, researchers use software capable of automatically generating thousands of tests. The purpose of these tests is to push the programs to their limits in order to identify any potential errors. 

Until now, however, these tools had a major limitation: whenever a test caused a software library to malfunction, the software responsible for running the tests would also stop working. This made it impossible to pinpoint the exact source of the problem or report it to the developers. 

Researchers at UNamur have therefore devised a new method. Now, each test is run separately. If one of them causes a crash, it is isolated: the testing software continues to run, logs the error, and carries on with its analysis. This makes it possible to automatically detect bugs that previously went unnoticed. 

Using this new method, researchers analyzed 1,648 modules from the leading machine learning libraries and uncovered 32 previously unknown bugs. Several of these affected software that has become indispensable for training artificial intelligence models.

These issues were reported to the development teams. Several have already been acknowledged and fixed. “When a bug is fixed at this level, potentially thousands, or even millions, of users benefit,” notes Xavier Devroey, a professor in the Faculty of Computer Science at UNamur and a member of the PReCISE research center at the Namur Digital Institute (NaDI). Beyond these fixes, this research helps make the software on which so many artificial intelligence applications rely today more reliable.

This breakthrough originated from Lucas Berg’s master’s thesis, completed at UNamur under the supervision of Professor Xavier Devroey. As part of his research internship, Lucas Berg then joined Professor Annibale Panichella’s team at TU Delft to work on Pynguin, the open-source automated test generation tool developed at the University of Passau by Professor Gordon Fraser’s team, mentioned earlier. 

Initially, his goal was to improve this tool. By preventing crashes while discovering bugs, this first contribution subsequently caught the attention of researchers at the University of Passau, who were facing the same problem, leading to an international collaboration that would result in a joint scientific publication. 

Lucas Berg is currently pursuing a Ph.D. at the Faculty of Computer Science at UNamur under the supervision of Professors Wim Vanhoof and Xavier Devroey. The results of this research were presented at the IEEE International Conference on Software Testing, Verification, and Validation (ICST 2026), one of the most prestigious international conferences in the field of software testing.  

This research also received support from the Wallonia Public Service (SPW Research) and Wallonia-Brussels International. This recognition underscores the quality of this work and its significance to the international scientific community. 

At NaDI, researchers develop innovative solutions to the new societal challenges posed by the digital revolution (eGov, eHealth, eServices, big data, etc.). Drawing from a variety of disciplines, the researchers combine their expertise in computer science, technology, ethics, law, management, and sociology. Comprising six research centers, the Namur Digital Institute offers unique multidisciplinary expertise in all areas of computer science, its applications, and its social impact.  

Artificial intelligence is now accessible to everyone. Yet, while many people use it every day, few truly understand how it works, its limitations, or the issues surrounding it. To address this need, the Namur Digital Institute (NaDI) is launching “What If AI…,” a series of lecture-workshops open to the general public, coordinated by doctoral students Elisa De Coster (PReCISE) and Aline Nardi (CRIDS). 

“The idea behind the project is to demystify artificial intelligence,” begins Elisa De Coster. “As researchers, we’re fortunate to be aware of the issues raised by AI. We wanted curious members of the public to have access to these keys to understanding as well.” 

“Before 2022 and the rise of ChatGPT, these tools were limited to a niche audience,” continues Aline Nardi. “Since then, the use of AI has become widespread at record speed, but reflection on the societal implications of these uses has not kept pace. This series of lectures is an invitation to take a step back and understand what’s happening behind the scenes.” 

Each session, lasting about two hours, will bring together the perspectives of several specialists. A doctoral student in computer science will explain the technical workings of AI, while his counterpart in law will address the legal, ethical, and societal implications.

Speakers from the professional world will share their real-world experience, and an interactive activity will allow the audience to put these concepts into practice. Interactivity, interdisciplinarity, and accessibility to the general public are the three pillars of the project.

Each event will address a different social issue: 

• October 1, 2026 – La Nef
  What if AI were your confidant?
  Can you confide in a chatbot? What are the risks? 
• October 22, 2026 – PA02
  What if AI were wrong?
  How can we recognize errors in artificial intelligence and maintain our critical thinking? 
• November 19, 2026 – Caméo
  What if AI were subjective? Is artificial intelligence
  truly neutral, or does it perpetuate certain biases? 
• February 25, 2027 – Quai 22
  What if AI stole your creations?
  What challenges does generative AI pose in terms of copyright and intellectual property? 
• March 18, 2027 – TRAKK
  What if AI knew you too well?
  What happens to our personal data, and to what extent can AI learn to know us? 

While Elisa De Coster and Aline Nardi coordinate the project, they emphasize that it is, above all, a collaborative effort. The lecture series is led by ten doctoral students from the PReCISE and CRIDS research centers: Elisa De Coster, Adélaïde Couplet, Jules Dejaeghere, Pierre Luycx, Pierre Poitier, Aline Nardi, Alix Gobert, Florian Jacques, Martin Rappe, Lionel Goffaux, and Olga Thiry. Together, they aim to help the general public better understand artificial intelligence, without demonizing it or presenting it as a miracle solution.  

Service marketing appealed to me because it directly touches people’s real lives: seeking medical care, learning, getting around, accessing public services, interacting with technology, or receiving support in times of vulnerability. Unlike product marketing, which focuses more on tangible goods, service marketing is concerned with experiences that are often intangible, subjective, and deeply human.

This field allows us to analyze what defines the quality of an experience: human or digital interactions, trust, emotions, the co-creation of value, as well as issues of social justice, ethics, inclusion, and dignity. The same experience can be perceived differently depending on the individual, their resources, their situation, and the context of their interaction. This is precisely what makes this field of research complex, fascinating, and essential. 

What motivates me today is understanding how organizations and institutions can design services that are not only effective or innovative, but also fairer, more humane, and more responsible. For me, service marketing is a tool for transforming people’s experiences and contributing, in very concrete ways, to people’s well-being and to a more inclusive society.

These questions are at the heart of my work. From this perspective, services are not reduced to a simple transaction but are situated within an ecosystem involving multiple stakeholders. For me, inclusion and equity are not abstract concepts: they prompt us to ask who is left out of certain services, who faces greater difficulties in being heard, understood, or served with dignity, and how to design interactions that strengthen autonomy rather than creating new forms of exclusion. My goal as a researcher is to address these issues and propose concrete recommendations in close collaboration with all stakeholders. In my view, academic research has a responsibility to engage with important societal issues and to help make the service system more inclusive and respectful of people’s dignity.

I also advocate for management research that is more open, more inclusive, and truly connected to the realities of the world. All too often, our field remains dominated by a Europe–United States lens, which leaves other contexts, other voices, and other experiences in the shadows. However, to remain relevant, management science must better integrate the diversity of human, social, cultural, and geographic realities. This commitment is not merely an intellectual conviction; it translates into concrete actions. For example, through the NEXSER program—an ambitious initiative supported by the European Union via Erasmus+—we have built a network of excellence with partners from the Global South. These collaborations—fueled by research stays, scholarly exchanges, and joint research projects—enable us to broaden our perspectives, challenge our established frameworks, and produce research that is more grounded, more accurate, and more useful. Whether through my institutional responsibilities, my international collaborations, or my engagement within my scientific community, I strive to contribute to academic ecosystems where scientific excellence, societal impact, and personal fulfillment advance hand in hand. For me, research without borders is also research that opens doors.

It is a true honor, and this recognition touches me deeply. This appointment serves as a reminder that academic impact is not measured solely by publications, but also by the ability to teach, support, and help others grow. It recognizes a central aspect of my academic identity: mentoring. 

I am convinced that supporting the next generation of researchers means shaping the future of research and society. It is essential, demanding work—yet often unseen. Helping young researchers find their voice in a highly competitive and demanding academic world, gain confidence, feel validated, and develop high-quality, meaningful research is, for me, just as important as conducting research myself. In a way, it means guiding them as they learn a demanding profession. This commitment is reflected in my participation in committees, thesis defense panels, and large-scale mentoring programs at the national and international levels.  When supervising my own doctoral students, I try to encourage them to nurture their passion for scientific research, to develop an academic mindset, to believe in their contributions, and to conduct research that transcends their individual trajectories to be of real benefit to society. For me, mentoring is not limited to the acquisition of scientific or methodological skills. It also involves passing on values that are fundamental to the academic world: integrity, rigor, responsibility, intellectual openness, respect for others… as well as a commitment to producing research that is useful, robust, and capable of meeting the highest scientific standards.

This nomination also holds special significance for me, as it comes from people I deeply respect, both in Belgium and elsewhere. The fact that senior colleagues and young researchers took the initiative to submit this nomination without my knowledge makes this recognition even more valuable and deeply personal. I sincerely enjoy interacting with young researchers. These interactions give great meaning to my academic mission. I enjoy taking the time to mentor them—whether they’re my Ph.D. students or not—with a level of expectation that I hope is always supportive. As I often tell them: “Small details make a big difference.” I am therefore deeply moved to see this long-term effort recognized by my peers.

My approach as a teacher aligns with the one I advocate as a researcher: helping students push themselves while bringing theoretical concepts to life. This approach shifts them from a mindset of simply absorbing content to one of active engagement. In this social innovation lab led by a highly dynamic team of instructors, students step into the role of “change makers” to develop solutions to pressing societal issues related to the Sustainable Development Goals, such as poverty, disability, and so on. We strive to bring them as close as possible to the issue at hand so they can shift their perspectives and better understand the realities experienced by others.  In this way, they learn to connect concepts to human realities, collaborate, experiment, take responsibility for their choices, and reflect on the consequences of their actions. We use innovative and immersive teaching methods. For example, we recently developed virtual reality scenarios that allow students to experience the daily life of a visually impaired person. In addition to stimulating their capacity for innovation, this course fosters their empathy—an essential quality for future leaders. I strongly believe in an educational approach that produces not only good students, but also citizens and future professionals capable of considering the impact of their actions. I am convinced that better training for our managers today contributes to building a better, fairer society that is more attentive to everyone.