With 24 official EU and many more additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by thousands of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented – by nation states, languages, verticals and sectors. Likewise, while much of European LT research is world-class, with results transferred into industry and commercial products, its full impact is held back by fragmentation. The key issue and challenge is the fragmentation of the European LT landscape. The European Language Grid (ELG) project will address this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG will be a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial Language Technologies for all European languages, including running tools and services as well as data sets and resources. It will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Through open calls, up to 20 pilot projects will be financially supported to demonstrate the usefulness of the ELG. The proposal is rooted in the experience of a consortium with partners involved in all relevant initiatives. Based on these, 30+ national competence centres and the European LT Board will be set up for European coordination. The ELG will foster “language technologies for Europe built in Europe”, tailored to our languages and cultures and to our societal and economical demands, benefitting the European citizen, society, innovation and industry.

RELIANCE will extend the EOSC service offering with a set of industry-strong, innovative, interconnected services for the open, efficient, and cross-disciplinary management of the research lifecycle. In accordance with FAIR and Open Science principles, it will adopt a holistic approach to address different research activities, spanning: discovery of and access to research data, methods and materials; extraction of valuable information from scientific literature; structuring data-driven scientific investigations as semantically rich research objects; FAIR-ness self-assessment; dynamic collaboration, sharing and dissemination considering the whole lifecycle as a first-class citizen; and supporting reproducibility, validation, versioning and reuse of research results. Core to the service portfolio are Research Objects as the overarching information artefact to manage research, Data Cubes for efficient and scalable structured data access and discovery, and Text Mining for the extraction of information from scientific text as machine-readable metadata. RELIANCE will pilot the services in three different Earth Science communities, fostering the use of Copernicus data and demonstrating their efficacy in real-life vertical and multi-disciplinary scenarios, and will launch an Open Call to engage other communities. The high-TRL research-enabling services portfolio will be onboarded in ESOC following established procedures, ensuring the interplay with cross-cutting and added value services, and following a user-centric approach. RELIANCE will expose its services via APIs and libraries, enabling their use via different frontends and Jupyter notebooks. RELIANCE brings into EOSC more than 3,000 research objects from research communities like Earth Science, represented by three project partners, but also others like Astrophysics and Bioinformatics, which have embraced research objects concept and tools, and will become the best ambassadors to enlarge and engage new communities

Artificial Intelligence (AI) is transforming research, industry, and society, with Large Language Models (LLMs) playing a central role. While LLMs excel in natural language understanding, reasoning, and content generation, they also exhibit hallucinations, security vulnerabilities, ethical concerns, and regulatory issues. These challenges are particularly critical in healthcare and education, where accuracy, reliability, and fairness are essential. Addressing these shortcomings requires AI paradigms that enhance interpretability, robustness, and compliance. THIRDWAVE aims to establish an international, interdisciplinary network to advance LLM-driven neuro-symbolic AI, integrating symbolic AI with LLMs to create interpretable, reliable, and domain-aware systems. This approach enables AI to leverage structured knowledge, improve decision-making, and comply with domain-specific constraints, making it more applicable to real-world challenges. The project is structured around four key objectives: O1) Understanding LLMs: Analyzing capabilities and limitations to improve performance, usability, and trustworthiness. O2) Enhancing LLMs: Improving fairness, factual accuracy, and robustness through external knowledge sources and human collaboration. O3) Advancing LLM-driven Neuro-Symbolic AI: Developing hybrid systems that combine LLMs with symbolic reasoning for structured knowledge representation and better decision support. O4) Use Cases & Evaluation: Applying LLM-driven neuro-symbolic AI in healthcare, education, geodata, and food information engineering, validating scalability and societal impact. By fostering collaboration among AI researchers, domain experts, and industry partners, THIRDWAVE will bridge the gap between data-driven and knowledge-driven AI, ensuring LLMs become interpretable, ethically aligned, and domain-aware. The project’s findings will inform AI regulation, advance research, and drive innovation, contributing to responsible AI development.