RoboCup 2026 saw history made, as two teams of 11 humanoids took to the soccer field, the first time a full complement of robots has competed. The game saw (Bremen, Germany) take on (Leipzig, Germany), with both sides using machines designed by Booster Robotics. Back in 1997, RoboCup's founders set the lofty goal of developing a team of autonomous robots that could beat the human World Cup champions by 2050. There has been significant progress since those early days, and this match saw another step towards that ambition. "This match shows how far humanoid robotics has come," said Ubbo Visser, President of the RoboCup Federation.

The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) took place in Bremen, Germany from 15-21 August. In the second of our social media round-ups we find out about the doctoral consortium, drop into the keynotes, and look back on an action-packed week. If you missed it, you can catch our round-up part 1 here . Today: presented my work, LLM Parametric Knowledge Gaps as a Democratic Harm, at the Augmented Democracies Workshop, IJCAI-ECAI 2026. Jaap Jumelet, UvA, was awarded the 2025 EurAI Dissertation Award for his outstanding PhD thesis Finding Structure in Language Models by the EurAI Board member and 2025 EurAI Disseration Award Chair Fredrik Heintz, https://t.co/Uo8YHTNSRK

The emergence of Large Language Models (LLMs) has revolutionized Conversational User Interfaces (CUIs), enabling more dynamic, context-aware, and human-like interactions across diverse domains, from social sciences to healthcare. However, the rapid adoption of LLM-based personas raises critical ethical and practical concerns, including bias, manipulation, and unforeseen social consequences. Unlike traditional CUIs, where personas are carefully designed with clear intent, LLM-based personas generate responses dynamically from vast datasets, making their behavior less predictable and harder to govern. This workshop aims to bridge the gap between CUI and broader AI communities by fostering a cross-disciplinary dialogue on the responsible design and evaluation of LLM-based personas. Bringing together researchers, designers, and practitioners, we will explore best practices, develop ethical guidelines, and promote frameworks that ensure transparency, inclusivity, and user-centered interactions. By addressing these challenges collaboratively, we seek to shape the future of LLM-driven CUIs in ways that align with societal values and expectations.

As early as 1949, Weaver defined communication in a very broad sense to include all procedures by which one mind or technical system can influence another, thus establishing the idea of semantic communication. With the recent success of machine learning in expert assistance systems where sensed information is wirelessly provided to a human to assist task execution, the need to design effective and efficient communications has become increasingly apparent. In particular, semantic communication aims to convey the meaning behind the sensed information relevant for Human Decision-Making (HDM). Regarding the interplay between semantic communication and HDM, many questions remain, such as how to model the entire end-to-end sensing-decision-making process, how to design semantic communication for the HDM and which information should be provided to the HDM. To address these questions, we propose to integrate semantic communication and HDM into one probabilistic end-to-end sensing-decision framework that bridges communications and psychology. In our interdisciplinary framework, we model the human through a HDM process, allowing us to explore how feature extraction from semantic communication can best support human decision-making. In this sense, our study provides new insights for the design/interaction of semantic communication with models of HDM. Our initial analysis shows how semantic communication can balance the level of detail with human cognitive capabilities while demanding less bandwidth, power, and latency.

Humanitarian action is accompanied by a mass of reports, summaries, news, and other documents. To guide its activities, important information must be quickly extracted from such free-text resources. Quantities, such as the number of people affected, amount of aid distributed, or the extent of infrastructure damage, are central to emergency response and anticipatory action. In this work, we contribute an annotated dataset for the humanitarian domain for the extraction of such quantitative information, along side its important context, including units it refers to, any modifiers, and the relevant event. Further, we develop a custom Natural Language Processing pipeline to extract the quantities alongside their units, and evaluate it in comparison to baseline and recent literature. The proposed model achieves a consistent improvement in the performance, especially in the documents pertaining to the Dominican Republic and select African countries. We make the dataset and code available to the research community to continue the improvement of NLP tools for the humanitarian domain.

The paper presents a novel cloud-based digital twin learning platform for teaching and training concepts of cognitive robotics. Instead of forcing interested learners or students to install a new operating system and bulky, fragile software onto their personal laptops just to solve tutorials or coding assignments of a single lecture on robotics, it would be beneficial to avoid technical setups and directly dive into the content of cognitive robotics. To achieve this, the authors utilize containerization technologies and Kubernetes to deploy and operate containerized applications, including robotics simulation environments and software collections based on the Robot operating System (ROS). The web-based Integrated Development Environment JupyterLab is integrated with RvizWeb and XPRA to provide real-time visualization of sensor data and robot behavior in a user-friendly environment for interacting with robotics software. The paper also discusses the application of the platform in teaching Knowledge Representation, Reasoning, Acquisition and Retrieval, and Task-Executives. The authors conclude that the proposed platform is a valuable tool for education and research in cognitive robotics, and that it has the potential to democratize access to these fields. The platform has already been successfully employed in various academic courses, demonstrating its effectiveness in fostering knowledge and skill development.

The notion of preferences plays an important role in many disciplines including service robotics which is concerned with scenarios in which robots interact with humans. These interactions can be favored by robots taking human preferences into account. This raises the issue of how preferences should be represented to support such preference-aware decision making. Several formal accounts for a notion of preferences exist. However, these approaches fall short on defining the nature and structure of the options that a robot has in a given situation. In this work, we thus investigate a formal model of preferences where options are non-atomic entities that are defined by the complex situations they bring about.

Geographic web sources such as OpenStreetMap (OSM) and knowledge graphs such as Wikidata are often unconnected. An example connection that can be established between these sources are links between streets in OSM to the persons in Wikidata they were named after. This paper presents StreetToPerson, an approach for connecting streets in OSM to persons in a knowledge graph based on relations in the knowledge graph and spatial dependencies. Our evaluation shows that we outperform existing approaches by 26 percentage points. In addition, we apply StreetToPerson on all OSM streets in Germany, for which we identify more than 180,000 links between streets and persons.

Many senses, smell, touch, hearing, and sight, can potentially be augmented, though the most common application of AR is sight, using a head-mounted display [2]. Several users may simultaneously access and operate a shared digitally augmented environment, either at the same place or remotely. Users commonly interact with each other and the augmented elements in this virtual framework by using hand gestures, movement, and even gaze. The interactive nature of AR, as well as its direct connection to the real world, have produced extensive research work and industrial applications of AR to different fields such as education, entertainment, medicine, and retail [6]. Human-Computer interaction in games (HCI-games) is a very broad field that covers research on the many ways in which human players interact with digital games that, given their interactive, playful, and challenging nature, present a rich field of study separated from human-computer interaction in other forms of software [1].

In Low Earth Orbit (LEO) mega constellations, there are relevant use cases, such as inference based on satellite imaging, in which a large number of satellites collaboratively train a machine learning model without sharing their local datasets. To address this problem, we propose a new set of algorithms based on Federated learning (FL), including a novel asynchronous FL procedure based on FedAvg that exhibits better robustness against heterogeneous scenarios than the state-of-the-art. Extensive numerical evaluations based on MNIST and CIFAR-10 datasets highlight the fast convergence speed and excellent asymptotic test accuracy of the proposed method.