arXiv AI June 29, 2026 By Yuzhe Wang, Yaochen Zhu, Jundong Li

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.

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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

Aug 11

TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation

arXiv:2510. 27544v3 Announce Type: replace Abstract: Current training paradigms, optimized for long-horizon reasoning trace execution, have made Large Language Models (LLMs) excel at pattern matching and forward simulation of reasoning, but underperform at counterfactual causal understanding and reasoning.

By Nikolaus Holzer, William Fishell, Baishakhi Ray, Mark Santolucito

llms agents benchmarks

Hugging Face Trending Papers

Jul 9

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant benchmark landscape largely divides into symbolic causal reasoning benchmarks without realistic data analysis or data analysis benchmarks without a principled causal data-generating structure.

llms agents benchmarks

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

Aug 6

Can Post-Training Transform LLMs into Causal Reasoners?

arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.

By Junqi Chen, Sirui Chen, Chaochao Lu

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 10

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

arXiv:2607. 08093v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use.

By Andrej Leban, Yuekai Sun

llms agents benchmarks

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Related stories

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

TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

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

Can Post-Training Transform LLMs into Causal Reasoners?

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents