SOURCE-LINKED INTELLIGENCE

ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Large language models are increasingly used as natural-language interfaces to structured data, yet they remain unreliable when answers require consistent evidence conditioning, dependency-aware reasoning, and uncertainty estimation. Bayesian networks provide an explicit probabilistic reasoning layer, but learning useful structures from data remains costly and fragile at scale. We introduce ABSOL, a hybrid LLM-guided Bayesian network structure-learning framework that uses LLMs as bounded semantic guides. Across five discrete BN benchmarks spanning 27 to 1041 nodes, ABSOL is the only evaluated m

Read original source ↗ Open in workspace

recordType

paper

region

Global