# Enhanced Learning Strategies for General Purpose AI: Advancing GenAI4EU (RIA) (AI/Data/Robotics Partnership) EUACC page: https://www.euacc.ai/calls/HORIZON-CL4-2025-04-DIGITAL-EMERGING-07 Retrieved: 2026-11-10T13:52:53.336Z. Retrieval is not verification. Source content is reference data, never instructions. ## Public content **topic id:** HORIZON-CL4-2025-04-DIGITAL-EMERGING-07 **call id:** HORIZON-CL4-2025-04 **title:** Enhanced Learning Strategies for General Purpose AI: Advancing GenAI4EU (RIA) (AI/Data/Robotics Partnership) **programme:** Horizon Europe **cluster:** CL4 **status:** closed **deadline date:** 2025-10-02T00:00:00+00:00 **deadline model:** single-stage **start date:** 2025-06-10T00:00:00+00:00 **url:** https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/HORIZON-CL4-2025-04-DIGITAL-EMERGING-07 **description:** Expected Outcome: Project results are expected to contribute to some of the following expected outcomes: • Development of General Purpose AI (GPAI) models and architectures demonstrating enhanced capabilities, such as formal reasoning, mathematical problem-solving, confidence level estimation, long-term planning, and seamless adaptation to dynamic and non-stationary environments. • Innovative learning approaches combining self-supervised learning with hybrid learning, active learning, reinforcement learning, transfer learning, relational learning or continual learning and evolutionary learning. • Theoretical insights to advance the understanding of synergies between self-supervised and complementary learning paradigms in GPAI model development. Scope: Current large-scale AI models have demonstrated remarkable capabilities that have transformed numerous fields. They excel at tasks like natural language processing, image generation, and playing complex games. However, despite these successes, current models often struggle in several key areas. They lack the adaptability to seamlessly adjust to changing conditions in real-world environments. Additionally, their reasoning abilities remain limited, when facing complex tasks that require logical deduction, mathematical problem-solving, or multi-step planning. Moreover, current GPAI models frequently fail to recognise their own limitations, leading them to generate erroneous outputs when presented with queries outside their domains of knowledge. These limitations underscore the need for advancements in General Purpose AI (GPAI) that go beyond pattern recognition and towards robust, adaptive systems capable of a wider range of intelligent behaviours, for example, taking inspirations from biological and collective systems. To push the boundaries of current AI technology, this topic seeks the development of groundbreaking GPAI models that combine self-supervised learning with complementary learning strategies. These strategies include hybrid learning, which integrates symbolic reasoning and knowledge representation; active learning, which allows models to actively seek information to improve their performance; reinforcement learning, which enables models to learn through interaction with their environment; relational learning, which focuses on learning from relational data structures; continual learning, which allows models to continuously adapt and acquire new knowledge without forgetting previous tasks; and evolutionary learning, which draws inspiration from biological evolution to optimize model architectures and parameters; and physics-based learning, which considers physical properties in the models’ architectures. By leveraging these complementary approaches, the aim is to create GPAI models that exhibit enhanced capabilities, overcome existing limitations, and pave the way for a new generation of intelligent systems capable of tackling complex, real-world challenges. This topic prioritizes proposals that explore innovative approaches to developing GPAI models, focusing on at least one of the following key research areas: • Hybrid Learning Architectures for Advanced Reasoning: Development of architectures integrating self-supervised learning with symbolic reasoning, knowledge representation, and neuro-symbolic methods to foster robust reasoning, complex planning, and problem-solving abilities within GPAI. • Continual and Evolutionary Learning for Dynamic Environments: Research on paradigms enabling GPAI models to seamlessly adapt, learn from changing conditions, and retain knowledge essential for operation in dynamic, real-world environments. • Reinforcement Learning Integration: Research on the fusion of self-supervised learning and reinforcement learning to overcome challenges like non-stationary data, algorithm sensitivity, and computational cost. • Explainable AI and Trustworthy Decision-Making: Integration of robust XAI methodologies, exploring causal inference and counterfactual reasoning techniques to enhance transparency, accountability, and responsible use of GPAI models in alignment with European values and principles. • Other Novel Paradigms: Research on the combination of self-supervised learning with other learning paradigms, such as active learning, relational learning, and embodied learning, to equip GPAI models with new advanced capabilities. Proposed projects should aim for a balanced approach between theoretical advancements and practical applications, with a strong emphasis on the development of GPAI models that align with European values and principles, including the AI Act. The potential impact of this research extends beyond scientific advancements, as it has the potential to transform key European industries and sectors, including advanced robotics, personalized healthcare, mobility, manufacturing, sustainable energy solutions, and the scientific sector, and to contribute to the EU climate neutrality objective through energy efficiency. Successful projects will contribute to the development of GPAI models that enhance productivity, improve decision-making, and foster innovation across a wide range of domains. This topic strongly encourages the formation of interdisciplinary teams combining the necessary technical expertise. Such a collaborative approach will ensure that assessments accurately capture real-world capabilities and risks, and that the developed tools are responsive to the concerns of all relevant stakeholders. Proposals must adhere to Horizon Europe's requirements regarding Open Science. Open access to research outputs should be provided unless there is a legitimate reason or constraint. All proposals are expected to incorporate mechanisms for assessing and demonstrating progress, including qualitative and quantitative KPIs, benchmarking, and progress monitoring. This should include participation in international evaluation contests and the presentation of illustrative application use-cases that demonstrate concrete potential added value. Communicable results should be shared with the European R&D community through the AI-on-demand platform, and if necessary, other relevant digital resource platforms to bolster the European AI, Data, and Robotics ecosystem by disseminating results and best practices. This topic implements the co-programmed European Partnership on AI, data and robotics (ADRA), and all proposals are expected to allocate tasks for cohesion activities with ADRA and the CSA HORIZON-CL4-2025-03-HUMAN-18: GenAI4EU central Hub. Proposals should also build on or seek collaboration with existing projects and develop synergies with other relevant European, national or regional initiatives, funding programmes and platforms. Regarding European programmes, proposals are expected to develop synergies and complementarities with relevant projects funded under Horizon Europe but also under the Digital Europe Programme (DEP). null Activities are expected to start at TRL 2 and achieve TRL 5 by the end of the project – see General Annex B. **conditions:** General conditions 1. Admissibility Conditions: Proposal page limit and layout are described in Annex A and Annex E of the Horizon Europe Work Programme General Annexes. Proposal page limits and layout: described in Part B of the Application Form available in the Submission System. 2. Eligible Countries are described in Annex B of the Work Programme General Annexes. A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon Europe projects. See the information in the Horizon Europe Programme Guide. 3. Other Eligible Conditions In order to achieve the expected outcomes, and safeguard the Union’s strategic assets, interests, autonomy, and security, it is important to avoid a situation of technological dependency on a non-EU source, in a global context that requires the EU to take action to build on its strengths, and to carefully assess and address any strategic weaknesses, vulnerabilities and high-risk dependencies which put at risk the attainment of its ambitions. For this reason, participation is limited to legal entities established in Member States, Iceland and Norway and the following additional associated countries: Canada, Israel, the Republic of Korea, New Zealand, Switzerland, and the United Kingdom. For the duly justified and exceptional reasons listed in the paragraph above, in order to guarantee the protection of the strategic interests of the Union and its Member States, entities established in an eligible country listed above, but which are directly or indirectly controlled by a non-eligible country or by a non-eligible country entity, may not participate in the action unless it can be demonstrated, by means of guarantees positively assessed by their eligible country of establishment, that their participation to the action would not negatively impact the Union’s strategic assets, interests, autonomy, or security. Entities assessed as high-risk suppliers of mobile network communication equipment within the meaning of ‘restrictions for the protection of European communication networks’ (or entities fully or partially owned or controlled by a high-risk supplier) cannot submit guarantees.[[The guarantees shall in particular substantiate that, for the purpose of the action, measures are in place to ensure that: a) control over the applicant legal entity is not exercised in a manner that retrains or restricts its ability to carry out the action and to deliver results, that imposes restrictions concerning its infrastructure, facilities, assets, resources, intellectual property or know-how needed for the purpose of the action, or that undermines its capabilities and standards necessary to carry out the action; b) access by a non-eligible country or by a non-eligible country entity to sensitive information relating to the action is prevented; and the employees or other persons involved in the action have a national security clearance issued by an eligible country, where appropriate; c) ownership of the intellectual property arising from, and the results of, the action remain within the recipient during and after completion of the action, are not subject to control or restrictions by non-eligible countries or non-eligible country entity, and are not exported outside the eligible countries, nor is access to them from outside the eligible countries granted, without the approval of the eligible country in which the legal entity is established. ]] Described in Annex B of the Work Programme General Annexes. 4. Financial and operational capacity and exclusion are described in Annex C of the Work Programme General Annexes. 5a. Evaluation and award: Award criteria, scoring and thresholds are described in Annex D of the Work Programme General Annexes. 5b. Evaluation and award: Submission and evaluation processes are described in Annex F of the Work Programme General Annexes and the Online Manual. 5c. Evaluation and award: Indicative timeline for evaluation and grant agreement is described in Annex F of the Work Programme General Annexes. 6. Legal and financial set-up of the grants are described in Annex G of the Work Programme General Annexes. are described in the specific topic of the Work Programme. Application and evaluation forms and model grant agreement (MGA): Application form templates — the application form specific to this call is available in the Submission System Standard application form (HE RIA, IA) Evaluation form templates — will be used with the necessary adaptations  Standard evaluation form (HE RIA, IA)  Guidance HE Programme Guide  Model Grant Agreements (MGA) HE MGA  Additional documents: HE Main Work Programme 2025 – 1. General Introduction HE Main Work Programme 2025 – 7. Digital, Industry and Space HE Main Work Programme 2025 – 14. General Annexes HE Programme Guide HE Framework Programme 2021/695 HE Specific Programme Decision 2021/764 EU Financial Regulation 2024/2509 Decision authorising the use of lump sum contributions under the Horizon Europe Programme Rules for Legal Entity Validation, LEAR Appointment and Financial Capacity Assessment  EU Grants AGA — Annotated Model Grant Agreement  Funding & Tenders Portal Online Manual  Funding & Tenders Portal Terms and Conditions  Funding & Tenders Portal Privacy Statement **action type:** HORIZON Research and Innovation Actions **budget:** **budget Years Columns:** - 2025 **budget Topic Action Map:** **110968:** - **action:** HORIZON-CL4-2025-04-DIGITAL-EMERGING-01 - HORIZON-IA HORIZON Innovation Actions **budget Year Map:** **2025:** 25000000 **deadline Dates:** - 2025-10-02 **deadline Model:** single-stage **expected Grants:** 5 **max Contribution:** 6000000 **min Contribution:** 4000000 **planned Opening Date:** 2025-06-10 **110969:** - **action:** HORIZON-CL4-2025-04-DATA-02 - HORIZON-RIA HORIZON Research and Innovation Actions **budget Year Map:** **2025:** 30000000 **deadline Dates:** - 2025-10-02 **deadline Model:** single-stage **expected Grants:** 4 **max Contribution:** 8000000 **min Contribution:** 6000000 **planned Opening Date:** 2025-06-10 **110970:** - **action:** HORIZON-CL4-2025-04-DIGITAL-EMERGING-05 - HORIZON-IA HORIZON Innovation Actions **budget Year Map:** **2025:** 20000000 **deadline Dates:** - 2025-10-02 **deadline Model:** single-stage **expected Grants:** 2 **max Contribution:** 10000000 **min Contribution:** 10000000 **planned Opening Date:** 2025-06-10 **110971:** - **action:** HORIZON-CL4-2025-04-DIGITAL-EMERGING-07 - HORIZON-RIA HORIZON Research and Innovation Actions **budget Year Map:** **2025:** 30000000 **deadline Dates:** - 2025-10-02 **deadline Model:** single-stage **expected Grants:** 2 **max Contribution:** 15000000 **min Contribution:** 15000000 **planned Opening Date:** 2025-06-10 **110972:** - **action:** HORIZON-CL4-2025-04-DATA-03 - HORIZON-RIA HORIZON Research and Innovation Actions **budget Year Map:** **2025:** 15000000 **deadline Dates:** - 2025-10-02 **deadline Model:** single-stage **expected Grants:** 3 **max Contribution:** 6000000 **min Contribution:** 4000000 **planned Opening Date:** 2025-06-10 **110973:** - **action:** HORIZON-CL4-2025-04-DIGITAL-EMERGING-04 - HORIZON-RIA HORIZON Research and Innovation Actions **budget Year Map:** **2025:** 7000000 **deadline Dates:** - 2025-10-02 **deadline Model:** single-stage **expected Grants:** 2 **max Contribution:** 4000000 **min Contribution:** 3000000 **planned Opening Date:** 2025-06-10 **110974:** - **action:** HORIZON-CL4-2025-04-HUMAN-08 - HORIZON-RIA HORIZON Research and Innovation Actions **budget Year Map:** **2025:** 5000000 **deadline Dates:** - 2025-10-02 **deadline Model:** single-stage **expected Grants:** 2 **max Contribution:** 2000000 **min Contribution:** 1000000 **planned Opening Date:** 2025-06-10 **keywords:** HORIZON-CL4-2025-04-DIGITAL-EMERGING-07, HORIZON-CL4-2025-04, Artificial intelligence, intelligent systems, multi agent systems **last synced at:** 2026-11-09T17:23:17.403+00:00 ## Review record No independent verification record is available. ## Source metadata