arXiv AI June 3, 2026 By Weixin Chen, Han Zhao

Causal Neural Probabilistic Circuits

arXiv:2603. 01372v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting the class label from the concept predictions.

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 Machine Learning

Aug 11

CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization

arXiv:2603. 24304v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios.

By Bowen Lu, Liangqiang Yang, Teng Li, Kun Zhang

diffusion benchmarks

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 Machine Learning

Jul 10

Certified Interventional Fidelity: Anytime-Valid, Adaptive Evaluation of Causal Claims in Mechanistic Interpretability

arXiv:2607. 08349v1 Announce Type: new Abstract: Mechanistic interpretability often evaluates explanations by intervening on a model: swapping hidden states, patching activations, ablating components, or comparing a compressed model to the original one.

By Amir Asiaee

llms safety

arXiv AI

Sep 10

When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference

arXiv:2609.06941v1 Announce Type: new Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundatio...

By Haohao Zhou

llms

arXiv AI

Jul 20

Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective

arXiv:2510. 05750v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have achieved remarkable success in node classification.

By Xiao Yang, Xuejiao Zhao, Zhiqi Shen

arXiv Machine Learning

Jun 5

LLM Explainability with Counterfactual Chains and Causal Graphs

arXiv:2606. 05972v1 Announce Type: new Abstract: Causal graphs provide a high-level language for making mechanisms transparent.

By Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor, Roi Reichart

llms nlp

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Related stories

CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization

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

Certified Interventional Fidelity: Anytime-Valid, Adaptive Evaluation of Causal Claims in Mechanistic Interpretability

When and Why LLM Causal Priors Help: Closed-Loop Prior Selection for Amortized Causal Inference

Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective

LLM Explainability with Counterfactual Chains and Causal Graphs