arXiv AI July 20, 2026 By Su Lan, Xuefei Yin, Yanming Zhu, Alan Wee-Chung Liew

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 12

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.

By Zihao Li, Fabrizio Russo

llms benchmarks safety

arXiv AI

Jun 29

CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

arXiv:2602. 20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations.

By Yuzhe Wang, Yaochen Zhu, Jundong Li

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

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 Machine Learning

Sep 10

CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

arXiv:2404.06349v3 Announce Type: replace Abstract: The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual r...

By Yu Zhou, Xingyu Wu, Jibin Wu, Liang Feng, Kay Chen Tan

llms benchmarks

arXiv AI

Jul 7

ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering

arXiv:2508. 21010v3 Announce Type: replace-cross Abstract: Existing Causal-Why Video Question Answering (VideoQA) models often struggle with higher-order reasoning, relying on opaque, monolithic pipelines that entangle video understanding, causal inference, and answer generation.

By Paritosh Parmar, Eric Peh, Basura Fernando

nlp benchmarks safety

One email a morning, machine-written

Related stories

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

CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

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

Implicit Causal Graph Construction in Text via Chain Discovery

CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering