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Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Te

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1 SIGNALFIRST DETECTED 31 March 2026
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31 Mar
Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach

arXiv:2603.26948v1 Announce Type: new Abstract: Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive

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28 Aug
EduRiskX: A Neuro-Symbolic Framework with F-Logic Reasoning for Early Academic Risk Prediction

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23 Mar
Stepwise: Neuro-Symbolic Proof Search for Automated Systems Verification

arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet

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18 May
Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems

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

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13 May
Hindsight Hint Distillation: Scaffolded Reasoning for SWE Agents from CoT-free Answers

arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit

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11 May
CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment

arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains

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12 Aug
Deep probabilistic logic programming for diagnostic reasoning from incomplete information: A case study in stroke detection

arXiv:2608.08561v1 Announce Type: new Abstract: In medical applications, raw data is frequently associated with significant privacy concerns, lending particular importance to the

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27 Apr
PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training

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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