LLM-Augmented Causal Discovery: Probabilistic Fusion of Edge Existence and Orien

arXiv:2609.19944v1 Announce Type: new Abstract: Large language models (LLMs) have been applied to causal discovery, but candidate-graph generation rarely treats premature omission

arXiv:2607.09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs)

arXiv:2605.00123v1 Announce Type: new Abstract: Safety trained large language models (LLMs) can often be induced to answer harmful requests through jailbreak prompts. Because we

arXiv:2605.15353v1 Announce Type: cross Abstract: Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in

arXiv:2605.05482v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly being adopted across various domains. However, their adoption in banking industry faces

arXiv:2603.27406v1 Announce Type: new Abstract: In this paper, the relationship between probabilistic graphical models, in particular Bayesian networks, and causal diagrams, also

arXiv:2604.22416v1 Announce Type: cross Abstract: Latent variables pose a fundamental challenge to causal discovery and inference. Conventional local methods focus on direct