Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction
A new framework, Causal-Audit, introduces explicit and auditable causal reasoning for large language models (LLMs) by constructing target-aware causal graphs and aggregating evidence from multiple reasoning paths. The method formulates causal inference as structured reasoning over explicit graphs, rather than relying on implicit language-level reasoning. Experiments on three benchmarks show that Causal-Audit outperforms existing LLM-based methods and provides interpretable, auditable reasoning traces.
Why it matters: This work offers a significant advance in making LLM reasoning more transparent and trustworthy by enabling structured, auditable causal inference.
Full story at: arXiv AI/ML ↗