arXiv AI August 10, 2026 By Dazhuo Qiu, Yingli Zhou, Amedeo Pachera, Angela Bonifati, Andrea Mauri

Toward a Causal Data Management Ecosystem for Decision Making and Agentic AI

arXiv:2608. 07214v1 Announce Type: cross Abstract: Modern AI is no longer a single model but an ecosystem: classical ML predictors, deep and multimodal models, large language models, and agents, each trained and tuned over different data sources and each producing outputs at scale that become inputs to the others.

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

arXiv AI

5d ago

A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

arXiv:2608. 13456v1 Announce Type: new Abstract: World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution.

By Avinash Kori, Fabrizio Russo

agents

Hugging Face Trending Papers

6d ago

A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from a causal perspective across multiple levels of abstraction, ranging from perceptual observations to building a conceptual representation of the structure governing the environment dynamics.

agents

arXiv AI

Jul 3

CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery

arXiv:2607. 01936v1 Announce Type: cross Abstract: Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions lead to causal identifiability issues.

By Nicholas Tagliapietra, Gian Lorenzo Marchioni, Moritz Willig, Juergen Luettin, Lavdim Halilaj, Kristian Kersting

rag agents

arXiv AI

Jul 7

CausalGame: Benchmarking Causal Thinking of LLM Agents in Games

arXiv:2607. 04293v1 Announce Type: cross Abstract: Building AI Scientist agents with Large Language Models (LLMs) has recently attracted growing attention.

By Zhenhao Chen, Yongqiang Chen, Chenxi Liu, Junchi Yu, Xiangchen Song, Zijian Li, Jialin Li, Philip Torr, Bo Han, Kun Zhang

llms agents benchmarks safety

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

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

Related stories

A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery

CausalGame: Benchmarking Causal Thinking of LLM Agents in Games

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

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