arXiv AI July 22, 2026 By Zhiming Xue, Yujue Wang, Menghao Huo

LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks

arXiv:2603. 04818v3 Announce Type: replace Abstract: Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritize forecasting accuracy without providing operationally interpretable early warnings.

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

llms

arXiv AI

Jul 22

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series

arXiv:2607. 18271v1 Announce Type: new Abstract: Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations.

By Ria Mundhra, Gustavo Sato dos Santos, Michael Benedikt

llms fine-tuning safety

arXiv Machine Learning

Jul 30

Forecasting Trajectory-Level Safety Risks in Black-Box Multi-Turn Interactions

arXiv:2607. 26820v1 Announce Type: new Abstract: As large language models (LLMs) evolve from standalone assistants into autonomous agents, ensuring their safety requires shifting beyond pointwise risk assessment to understand how risks emerge and unfold over long-horizon trajectories.

By Shi Lin, Peng Qian, Dinghao Liu, Renjie Sun, Sifan Wu, Dezhang Kong, Chenpei Wang, Xun Wang

llms agents safety

arXiv AI

Jul 15

G-SHARE: A Guideline-Based Structured Reasoning Framework for Human-Factor Event Diagnosis

arXiv:2607. 11892v1 Announce Type: cross Abstract: Human-factor event diagnosis is essential for learning from operational events in nuclear power plants, yet its quality depends strongly on expert interpretation of narrative reports and guideline-based reasoning.

By Xingyu Xiao, Mao Du, Jiejuan Tong, Jingang Liang, Haitao Wang

llms benchmarks safety

arXiv AI

Jun 11

WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

arXiv:2606. 11816v1 Announce Type: cross Abstract: Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information.

By Yizhou Chi, Eric Chamoun, Zifeng Ding, Andreas Vlachos

llms agents benchmarks

arXiv AI

Jul 17

CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment

arXiv:2607. 14416v1 Announce Type: new Abstract: The interconnected nature of global financial systems makes them vulnerable to systemic risks, where the failure of a few institutions can trigger catastrophic cascading defaults.

By Rabimba Karanjai, Hemanth Madhavarao, Lei Xu, Weidong Shi

safety

arXiv AI

Jul 13

All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models

arXiv:2607. 09502v1 Announce Type: cross Abstract: Explaining machine-learning models is increasingly important for decision-making and consumer trust, yet it is widely believed to come at a cost: existing Explainable AI (XAI) methods suffer from a persistent accuracy-explainability trade-off.

By Pan Li

llms agents benchmarks

Related stories

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series

Forecasting Trajectory-Level Safety Risks in Black-Box Multi-Turn Interactions

G-SHARE: A Guideline-Based Structured Reasoning Framework for Human-Factor Event Diagnosis

WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

CausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment

All Explanations are Wrong, But Many Are Useful: Exploring the Rashomon Explanation Set with Large Language Models