arXiv:2603.26948v1 Announce Type: new Abstract: Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive
arXiv:2608.26107v1 Announce Type: new Abstract: Predicting students' academic risk in online education is crucial for enabling timely interventions that can improve retention and
arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet
arXiv:2605.16198v1 Announce Type: new Abstract: We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI
arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit
arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains
arXiv:2608.08561v1 Announce Type: new Abstract: In medical applications, raw data is frequently associated with significant privacy concerns, lending particular importance to the
arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining
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