arXiv AI August 12, 2026 By Zihao Li, Fabrizio Russo

Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach

arXiv:2602. 16481v2 Announce Type: replace Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 20

Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction

arXiv:2607. 15281v1 Announce Type: new Abstract: Causal and intervention-based question answering is fundamental to advancing large language models (LLMs) toward reasoning beyond surface-level correlations and understanding underlying causal mechanisms.

By Su Lan, Xuefei Yin, Yanming Zhu, Alan Wee-Chung Liew

llms nlp benchmarks

arXiv AI

Jun 9

Implicit Causal Graph Construction in Text via Chain Discovery

arXiv:2606. 07525v1 Announce Type: cross Abstract: Causal graphs in text are typically populated by observable, predefined events.

By Liesbeth Allein, Marie-Francine Moens

llms

arXiv AI

Jun 10

Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

arXiv:2606. 10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making.

By Xinyu Li, Yuanyuan Wang, Haoxuan Li, Chuan Zhou, Erdun Gao, Bo Han, Tongliang Liu, Kun Zhang, Howard Bondell, Mingming Gong

llms agents safety

arXiv Machine Learning

Jun 26

Use What You Know: Causal Foundation Models with Partial Graphs

arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.

By Arik Reuter, Anish Dhir, Cristiana Diaconu, Jake Robertson, Ole Ossen, Frank Hutter, Adrian Weller, Mark van der Wilk, Bernhard Sch\"olkopf

arXiv AI

Jun 2

Consistency evaluation of benchmarks used for causal discovery

arXiv:2606. 01789v1 Announce Type: new Abstract: In graphical causal model, causal discovery aims to construct a causal graph based on numerical data and domain knowledge in plain text.

By Yuzhe Zhang, Chihui Chen, Lina Yao, Chen Wang

llms benchmarks

arXiv AI

Jun 24

Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization

arXiv:2605. 01482v3 Announce Type: replace Abstract: Multi-Hop Fact Verification requires complex reasoning across disparate evidence, posing significant challenges for Large Language Models , which may suffer from hallucinations and fractured logical chains.

By Yunhan Bu, Quan Zhang, Huaping Zhang, Guotong Geng, Chunxiao Gao, Askar Hamdulla, Juan Wang, Qiuchi Li, Baohua Zhang, Shuai Lei, Yunbo Cao, Zhunchen Luo

llms reinforcement-learning safety

Related stories

Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction

Implicit Causal Graph Construction in Text via Chain Discovery

Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

Use What You Know: Causal Foundation Models with Partial Graphs

Consistency evaluation of benchmarks used for causal discovery

Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization