arXiv AI June 17, 2026 By Matthew Francis Dixon

Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation

arXiv:2606. 17383v1 Announce Type: cross Abstract: Agentic artificial intelligence systems introduce a new class of model risk.

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

arXiv Machine Learning

Jun 8

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

arXiv:2511. 02748v2 Announce Type: replace-cross Abstract: We argue that sixth-generation (6G) intelligence is not fluent token prediction but the capacity to imagine and choose -- to simulate future scenarios, weigh trade-offs, and act with calibrated uncertainty.

By Farhad Rezazadeh, Amir Ashtari Gargari, Hatim Chergui, Sandra Lagen, Merouane Debbah, Houbing Song, Lingjia Liu

llms agents

arXiv AI

Jul 13

Risk-Aware General-Utility Markov Decision Processes

arXiv:2607. 09298v1 Announce Type: cross Abstract: We study general-utility Markov decision processes (GUMDPs) with risk-aware objectives.

By Pedro P. Santos, F\'abio Vital, Alberto Sardinha, Francisco S. Melo

agents reinforcement-learning

arXiv AI

Jul 17

Decision Making Needs Uncertainty Quantification [Lecture Notes]

arXiv:2607. 14407v1 Announce Type: cross Abstract: Many signal processing systems ultimately exist to {act}.

By Osvaldo Simeone

agents

arXiv Machine Learning

Jun 3

Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.

By Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar, Pascal Van Hentenryck

arXiv AI

Jun 2

Bridging the Last Mile of Time Series Forecasting with LLM Agents

arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.

By Yuhua Liao, Zetian Wang, Qiangqiang Nie, Zhenhua Zhang

llms agents safety

arXiv AI

Jul 29

Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks

arXiv:2607. 25877v1 Announce Type: new Abstract: This paper investigates how multi-agent systems (MAS)-based on large language models (LLMs) can support actuarial risk modelling, with a particular focus on uncertainty quantification.

By Bart Custers, Koorosh Aslansefat

llms agents

Related stories

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

Risk-Aware General-Utility Markov Decision Processes

Decision Making Needs Uncertainty Quantification [Lecture Notes]

Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

Bridging the Last Mile of Time Series Forecasting with LLM Agents

Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks