arXiv AI July 7, 2026 By Guilhem Fouilh\'e, Rebecca Eifler, Antonin Poch\'e, Sylvie Thi\'ebaux, Nicholas Asher

Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning

arXiv:2603. 02070v3 Announce Type: replace Abstract: When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human's role is to guide the AI planner according to their preferences and expertise.

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

arXiv AI

Jun 29

Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework

arXiv:2606. 27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliability.

By Jiajing Zhang, Jiamei Jiang, Chenyang Zhang, Feifei Mo, Linjing Li, Daniel Zeng

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arXiv AI

Jul 3

From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents

arXiv:2604. 19775v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments.

By Trilok Padhi, Ramneet Kaur, Krishiv Agarwal, Adam D. Cobb, Daniel Elenius, Manoj Acharya, Colin Samplawski, Alexander M. Berenbeim, Nathaniel D. Bastian, Susmit Jha, Ugur Kursuncu, Anirban Roy

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arXiv Machine Learning

Jun 3

Multi$^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments

arXiv:2606. 03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments.

By Sangeun Park, Minhae Kwon

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arXiv AI

Jun 9

A Survey on Large Language Model-Based Game Agents

arXiv:2404. 02039v5 Announce Type: replace Abstract: Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity.

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llms agents

arXiv AI

Jun 9

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

arXiv:2605. 12213v2 Announce Type: replace Abstract: LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context.

By Jiazhou Liang, Armin Toroghi, Yifan Simon Liu, Faeze Moradi Kalarde, Liam Gallagher, Scott Sanner

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arXiv AI

Jul 29

ODYSSE: Episode-wise Policy Optimization for Personalized Agentic Reasoning

arXiv:2607. 25369v1 Announce Type: new Abstract: Agentic systems have rapidly advanced in their ability to interact with real-world environments, leverage external tools, and provide services for users.

By Jiaqi Zhang, Tong Chen, Junliang Yu, Quoc Viet Hung Nguyen, Hongzhi Yin

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Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework

From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents

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