BFS-based LLM framework reduces causal graph discovery queries from quadratic to linear while incorporating observational data and reporting state-of-the-art results on real graphs.
Prompting large language models for counterfactual generation: An empirical study
2 Pith papers cite this work. Polarity classification is still indexing.
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Blockchain and AI integration for network security has strong conceptual fit but mostly prototype-level evidence; the paper offers a taxonomy, integration patterns, and the BASE evaluation checklist to organize the field.
citing papers explorer
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Efficient Causal Graph Discovery Using Large Language Models
BFS-based LLM framework reduces causal graph discovery queries from quadratic to linear while incorporating observational data and reporting state-of-the-art results on real graphs.
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Blockchain and AI: Securing Intelligent Networks for the Future
Blockchain and AI integration for network security has strong conceptual fit but mostly prototype-level evidence; the paper offers a taxonomy, integration patterns, and the BASE evaluation checklist to organize the field.