DeepRAP: Deep Reasoning, Abstraction & Planning towards trustworthy Cognitive AI Systems

In addition, proposals will: Propose new methods and metrics for evaluating and certifying reasoning and trustworthiness in AI as well as the use of the computational resources Follow the FAIR principles ensuring all data, models, and results are Findable, Accessible, Interoperable, and Reusable to maximise transparency, reproducibility, and impact, and Develop synergies with EU initiatives such as TEFs (AI Testing and Experimentation Facilities) [3] , eBrains [4] , Resource for AI Science in Europe (RAISE) [5] , AI-on-demand Platform (AIoD) [6] and the Quantum Flagship [7] . Portfolio approach The composition of the portfolio of projects to be funded under the DeepRAP Challenge will ensure comprehensive coverage across the following categories with a view to ensuring breadth and enabling synergies between the projects: Category 1 – Cognitive Function Capability: Reasoning, abstraction, and planning should be covered by the selected portfolio. Category 2 – Technological Approach: The selected projects are expected to use a variety of technological approaches, including but not limited to, neuro-symbolic AI, deep learning, reinforcement learning, and novel frameworks inspired by interdisciplinary fields, and Category 3 – Use Case and Application Domain: The selected projects will cover a variety of real-world domains, such as industry, mobility, civil security, scientific discovery, health, cybersecurity, justice and human-robot interaction. The selected projects will also be assigned to lead and/or engage in portfolio activities centred on the following priorities: Interoperability: Establishing common standards and protocols to ensure seamless alignment between projects Benchmark Development: Co-creating a DeepRAP benchmark with shared tasks and an open evaluation platform for transparent assessment Common Pilots: Delivering joint pilot demonstrations addressing complex real-world problems to showcase DeepRAP capabilities Multiagent Integration: where feasible, combining project outcomes into modular, multiagent AI systems demonstrating collective reasoning and planning through structured interactions among multiple agents Application Shaping: Defining impactful use cases and engaging stakeholders to guide the development and adoption of innovative cognitive AI systems, and Ethical and Societal Alignment: Proactively addressing ethical, legal, and societal considerations, including fundamental rights, transparency, privacy, safety, and fairness of cognitive AI systems. Objective: Innovative ideas put forward under this Challenge must explore novel approaches, including combinations of existing techniques (i.e. neuro-symbolic AI), or the creation of entirely new frameworks that go beyond current, traditional, deep learning and reinforcement learning paradigms. These could be inspired by developments in diverse fields such as neuroscience, biology, physics, philosophy and more. The proposals should address one or more of the following cognitive capabilities: Deep Reasoning: Moving beyond statistical pattern matching to support causal inference, logical reasoning, and context-aware or commonsense decision-making in complex, unstructured environments. This requires shifting from purely data-driven correlations to AI systems capable of understanding why patterns emerge, identifying underlying causes, and drawing valid conclusions through both deductive and inductive processes. Neuro-symbolic approaches, which combine the learning power of neural networks with the structured inference of symbolic reasoning are particularly encouraged to advance these capabilities. Integrating contextual and commonsense knowledge enables AI to interpret information more holistically, adapt decisions dynamically, and handle ambiguity and uncertainty. Deep reasoning systems should be able to reconcile multiple sources of information, provide transparent and explainable rationales for their outputs, and align with human values and expectations, ensuring trustworthy and accountable operations in demanding real-world scenarios. Deep Abstraction: Enabling AI systems to generalise insights from limited data by forming, manipulating, and refining high-level concepts, analogies, and representations that can be transferred across diverse application domains. This includes the development of internal world models to support abstraction, foster commonsense understanding, and integrate semantic and contextual awareness. Approaches that combine symbolic reasoning, analogical mapping, and representation learning are particularly encouraged, as they empower AI to interpret meaning, intent, and relationships within complex environments. Progress in deep abstraction is essential for achieving cognitive flexibility, robust transfer learning, and adaptive reasoning in dynamic, data-scarce, or rapidly evolving settings. Deep Planning: Developing robust, adaptive, and scalable planning algorithms/models capable of operating in open-world, agentic, or uncertain real-time environments. This involves leveraging advanced deep learning techniques such as deep reinforcement learning and architectures tailored for planning tasks to enable AI systems to autonomously devise, optimise, and adjust complex strategies in dynamic settings. Neuro-symbolic approaches integrating neural networks with symbolic reasoning are particularly encouraged to address uncertainty, provide formal guarantees, and enable explainable, dependable decision-making. Emphasis is placed on long-term, flexible planning that incorporates cognitive timing and predictive modelling, enabling systems to anticipate and adapt within dynamic contexts. Approaches should explore hierarchical planning across multiple temporal levels, contingency planning for effective fallback strategies, and continual re-planning to dynamically update plans as environments evolve. These advancements will underpin resilient, coordinated, and trustworthy AI planning in complex, unpredictable scenarios. Scope: Artificial Intelligence (AI) systems have achieved remarkable progress as evidenced by the ability of Generative AI to recognise patterns and generate contextually relevant outputs based on ever larger models and associated datasets. However, despite the remarkable strides made over the past decade, there remains a significant gap between the capabilities of the human brain and machine intelligence, which must be overcome to achieve robust performance and enable effective interactions with users and stakeholders. Current Generative AI models can release very accurate outputs and even solve some mathematical problems but might struggle with some complex reasoning benchmarks and to understand the real world. These models frequently fail to reliably solve logic tasks and long-term planning, even when provably correct solutions exist, limiting their effectiveness in critical applications where precision is essential. Inspired by the human brain’s ability to process information at multiple levels of abstraction—enabling perception, reasoning, and goal-directed planning—the goal of this Challenge is to move beyond the current state-of-the-art in traditional AI approaches, whether symbolic (e.g., rules, decision trees, symbolic regression, etc.) or connectionist, neural (e.g., deep learning, large language models, reinforcement learning). The goal is to significantly improve the Reasoning, Abstraction, and Planning (RAP) capabilities of AI systems. This will overcome the limitations of current deep learning models, which despite their strengths, have limitations in critical cognitive functions for abstraction, contextualisation, causality, explainability, and intelligible reasoning — competencies that are fundamental to move towards human-like intelligence. [1] https://digital-strategy.ec.europa.eu/en/news/commission-launches-ai-innovation-package-support-artificial-intelligence-startups-and-smes [2] https://ec.europa.eu/commission/presscorner/detail/en/ip_24_383 150 AI Act | Shaping Europe’s digital future (europa.eu) [3] Sectorial AI Testing and Experimentation Facilities under the Digital Europe Programme | Shaping Europe’s digital future [4] EBRAINS: Europe's Research Infrastructure for Brain Research - EBRAINS [5] [6] Home Page | AI-on-Demand [7] Introduction to the Quantum Flagship | Quantum Flagship

Eligibility and submission conditions

"> General conditions 1. Admissibility conditions: Proposal page limit and layout In order to apply, your proposal must meet the general eligibility requirements (see Annex 2 of EIC Work Programme 2026 ) as well as specific eligibility requirements for the Challenge (please see the Topic description above). Please check for particular elements (e.g., specific application focus or technology) in the respective Challenge chapter. The EIC Pathfinder Challenges support collaborative or individual research and innovation from consortia or from single legal entities established in a Member State or an Associated Country (unless stated otherwise in the specific Challenge chapter). In case of a consortium your proposal must be submitted by the coordinator on behalf of the consortium. Consortia of two entities must be comprised of independent legal entities from two different Member States or Associated Countries. Consortia of three or more entities must include as beneficiaries at least three legal entities, independent from each other and each established in a different country as follows: at least one legal entity established in a Member State; and at least two other independent legal entities, each established in different Member States or Associated Countries. The legal entities may for example be universities, research organisations, SMEs, start-ups, natural persons. In the case of single beneficiary projects, mid-caps and larger companies will not be permitted. Applications with elements that concern the evolution of European communication networks (5G, post-5G and other technologies linked to the evolution of European communication networks) will be subject to restriction for the protection of European communication networks (see Annex II – Section B1). The standard admissibility and eligibility conditions and the eligibility of applicants from third countries are detailed in Annex 2. Proposal page limit and layout: Described in Part B of the Application Form available in the Submission System. Sections 1 to 3 of the part B of your proposal, corresponding respectively to the evaluation criteria Excellence, Impact, and Quality and Efficiency of the Implementation, must consist of a maximum of 30 format A4 pages. Excess pages will be automatically made invisible, and will not be taken into consideration by the evaluators. Please also consult Annex 2 of the EIC Work Programme 2026 . 2. Eligible countries Described in Annex 2 of the EIC Work Programme 2026 . 3. Other Eligibility Conditions Described in Annex 2 of the EIC Work Programme 2026 . 4. Financial and operational capacity and exclusion Described in Annex 2 of the EIC Work Programme 2026 . 5a. Evaluation and award: Award criteria, scoring and thresholds Described in Section II of the EIC Work Programme 2026 . 5b. Evaluation and award: Submission and evaluation processes Described in Section II of the EIC Work Programme 2026 and the Online Manual . 5c. Evaluation and award: Indicative timeline for evaluation and grant agreement Described in Section II of the EIC Work Programme 2026 . 6. Legal and financial set-up of the grants Please refer to the Lump Sum Model Grant Agreement (Lump Sum MGA) used for Lump Sum EIC actions under Horizon Europe. Specific conditions Described in the EIC Work Programme 2026 . Call Documents: EIC Work Programme 2026 Frequently Asked Questions (FAQs) Standard application form (HE EIC Pathfinder Challenges) Standard evaluation form (HE EIC Pathfinder Challenges) Challenge Guide: Deep Reasoning, Abstraction & Planning towards trustworthy Cognitive AI Systems Model grant agreement (MGA): Lump Sum MGA Call-specific instructions Detailed budget table (HE LS) Information on clinical studies (HE) Guidance: "Lump sums - what do I need to know?" Additional documents: HE Programme Guide HE Framework Programme 2021/695 HE Specific Programme Decision 2021/764 EU Financial Regulation 2018/1046 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

Hivatkozott dokumentumok

EIC Work Programme 2026 Online Manual Standard application form (HE EIC Pathfinder Challenges) Standard evaluation form (HE EIC Pathfinder Challenges) Challenge Guide: Deep Reasoning, Abstraction & Planning towards trustworthy Cognitive AI Systems Lump Sum MGA Detailed budget table (HE LS) Information on clinical studies (HE) Guidance: "Lump sums - what do I need to know?" HE Programme Guide Rules for Legal Entity Validation, LEAR Appointment and Financial Capacity Assessment EU Grants AGA — Annotated Model Grant Agreement Funding & Tenders Portal Terms and Conditions