arXiv:2603.27076v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for automated tutoring, but their reliability in structured symbolic domains remains unclear. We study step-level feedback for propositional logic proofs, which require precise symbolic reasoning aligned with a…
arXiv:2603.28306v1 Announce Type: cross Abstract: Self++ is a design blueprint for human-AI symbiosis in extended reality (XR) that preserves human authorship while still benefiting from increasingly capable AI agents. Because XR can shape both perceptual evidence and action, apparently ‘helpful’…
arXiv:2603.26983v1 Announce Type: new Abstract: Art. 50 II of the EU Artificial Intelligence Act mandates dual transparency for AI-generated content: outputs must be labeled in both human-understandable and machine-readable form for automated verification. This requirement, entering into force in August…
arXiv:2603.26996v1 Announce Type: new Abstract: We present FormalProofBench, a private benchmark designed to evaluate whether AI models can produce formally verified mathematical proofs at the graduate level. Each task pairs a natural-language problem with a Lean~4 formal statement, and a…
arXiv:2603.26948v1 Announce Type: new Abstract: Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical…
arXiv:2603.26944v1 Announce Type: new Abstract: Predictive modeling on sequential event data is critical for fraud detection and healthcare monitoring. Existing data-driven approaches learn correlations from historical data but fail to incorporate domain-specific sequential constraints and logical rules governing event relationships,…
arXiv:2603.26838v1 Announce Type: new Abstract: This paper surveys uncertainty-aware explainable artificial intelligence (UAXAI), examining how uncertainty is incorporated into explanatory pipelines and how such methods are evaluated. Across the literature, three recurring approaches to uncertainty quantification emerge (Bayesian, Monte Carlo,…
arXiv:2603.21440v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge-intensive reasoning tasks. Knowledge Base Question Answering (KBQA), which leverages structured Knowledge Graphs (KGs) exemplifies this challenge due to the need for…
arXiv:2603.27116v1 Announce Type: new Abstract: Every major AI memory system in production today organises information by meaning. That organisation enables generalisation, analogy, and conceptual retrieval — but it comes at a price. We prove that the same geometric structure enabling…
arXiv:2603.28123v1 Announce Type: cross Abstract: Constitutional AI (CAI) aligns language models with explicitly stated normative principles, offering a transparent alternative to implicit alignment through human feedback alone. However, because constitutions are authored by specific groups of people, the resulting models…
