The workshop accepts both regular submissions that will be published in the CEUR Workshop Proceedings, as well as non-archival submissions (that will not appear in conference proceedings) to facilitate dissemination of results, discussion and cross-breeding of communities with radically different publishing habits. 

Each accepted contribution (archival or non-archival) will be presented by an Author registered to the Workshop/Conference.

We accept submissions under the forms of

• Regular Contributions (12 pages, single column), discussing fully fledged studies  and that will appear in the workshop proceedings

• Short Papers (6 pages, single column), reporting early results of ongoing works or discussion topics to foster debate, and that will also appear in the workshop proceedings

• Non-Archival (12 pages, single column), reporting results of a relevant recent work (published or submitted in other venues); these paper WILL NOT appear in the workshop proceedings

All submissions should be in PDF format: irrespective of the nature, all submissions will receive 3 reviews. Peer-review will be single blind and will be handled through EasyChair.

• Submission format: All manuscripts, irrespectively of their nature (Regular, Extended Abstract, Non-Archival), need to be formatted according to the CEURART style. 

• Submission portal: Submissions are handled via Easychair. Direct link: https://easychair.org/conferences?conf=ala2025

A list of (tentative and non-exhaustive) topics of interest for the workshop follows:

• Generalization Through Awareness: How awareness can enhance adaptability to dynamic and unseen scenarios, advancing the generalization capabilities of ML models.

• Architectures for Computational Awareness: from computational frameworks to concrete computational implementations of machine awareness.

• Self-Awareness in Learning Systems: Developing methods for agents to introspect, identify gaps in understanding, and autonomously refine their learning processes.

• Measures of Awareness: Definition of measures and processes to evaluate machine awareness, including task-related performance, computational and behavioural measures. 

• Trust, Transparency, and Explainability: The role of awareness in improving the interpretability of AI systems, making their reasoning and actions more comprehensible and trustworthy.

• Collaborative Systems: Investigating how awareness allows agents in multi-agent systems to share context, coordinate actions, and work cohesively in dynamic and uncertain environments, leading to improved collaboration and efficiency.

• Design Patterns for Neural Networks: Exploring how awareness can inspire new paradigms of modular, scalable, and efficient neural networks.

• Synergy of Awareness and Learning Paradigms: Exploring intersections with meta-learning, continual learning, and reinforcement learning to embed awareness into AI frameworks.

• Real-World Applications: Case studies highlighting the value of awareness in robotics, collaborative systems, safety-critical environments, and adaptive user-facing technologies.

• Redefining Machine Intelligence: What does it mean for a system to be "aware"? How do these advancements challenge existing definitions of intelligence, consciousness, and autonomy?

• Ethical Dilemmas: Awareness in AI systems raises questions about agency, accountability, and moral decision-making. How can we ensure that aware agents act ethically in complex, real-world scenarios?

• Societal Trust and Acceptance: The integration of aware systems in daily life may evoke public skepticism. How can awareness enhance trust, and what are the risks of misuse or overreliance on such systems?

• Impact on Human-AI Collaboration: How will the presence of awareness in learning agents reshape human-AI interactions, roles, and dependencies in various domains?

• Regulation and Governance: The workshop will consider the policy and regulatory challenges posed by aware systems, aiming to propose frameworks for responsible development and deployment.