A neuro-symbolic agent system for requirements reuse achieves 100% coverage and 0.2% constraint violations by construction through symbolic enforcement of an OOMRAM lattice.
Leveraging graph-rag and prompt engineering to enhance llm-based automated requirement traceability and compliance checks
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
SLEID combines Isolation Forest and iterative self-training to detect illicit accounts in large-scale Ethereum DeFi transactions, achieving better precision and F1 than baselines while using less labeled data.
citing papers explorer
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Neuro-Symbolic Agents for Hallucination-Free Requirements Reuse
A neuro-symbolic agent system for requirements reuse achieves 100% coverage and 0.2% constraint violations by construction through symbolic enforcement of an OOMRAM lattice.
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Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions
SLEID combines Isolation Forest and iterative self-training to detect illicit accounts in large-scale Ethereum DeFi transactions, achieving better precision and F1 than baselines while using less labeled data.