arXiv AI July 3, 2026 By Tianyi Zhang, Mousumi Das, Abrar Anwar, Jesse Thomason, David Traum

DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents

arXiv:2607. 01557v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with persuasion in high-stakes scenarios.

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

arXiv AI

Jun 15

UP-NRPA: User Portrait based Nested Rollout Policy Adaptation for Planning with Large Language Models in Goal-oriented Dialogue Systems

arXiv:2606. 13683v1 Announce Type: new Abstract: To address the challenge that current dialogue policy planning methods struggle to dynamically adapt to diverse user characteristics, this paper proposes a User Portrait based Nested Rollout Policy Adaptation (UP-NRPA) online framework with Large Language Models.

By Hui Wang, Fafa Zhang, Meng Liu, Xiangyu Chen, Chaoxu Mu

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 3

$\Psi$-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues

arXiv:2606. 02754v1 Announce Type: new Abstract: Personalization is a crucial capability of modern language agents.

By Peixuan Han, Hongyi Du, Jiayu Liu, Yihang Sun, Yutong Liu, Jiaxuan You

llms agents benchmarks

arXiv AI

Jul 1

LLM-Driven Personalities for Decision Making in Emergency Simulations

arXiv:2606. 31038v1 Announce Type: cross Abstract: For virtual humans to appear believable, they must exhibit agency and spatial awareness while interacting with their environment in ways that reflect competence and intelligence.

By Stefano Calzolari, Rubens Montanha, Gabriel Schneider, Gustavo Wide, Paulo Knob, Francesco Strada, Andrea Bottino, Soraia Raupp Musse

llms agents

arXiv AI

Aug 13

Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

arXiv:2608. 11624v1 Announce Type: cross Abstract: Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions.

By Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur, Dilek Hakkani-T\"ur

llms agents reinforcement-learning safety

arXiv AI

Jul 24

Robust Critics: Defending LLMs Against Multi-Turn Attacks

arXiv:2607. 20472v1 Announce Type: new Abstract: When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question?

By Roman Belaire, Arunesh Sinha, Pradeep Varakantham

llms reinforcement-learning fine-tuning safety

arXiv AI

2d ago

Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation

arXiv:2608. 15949v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendation accuracy and natural dialogue.

By Cedar Site Bai, Duanshun Li, Zhenyu Liao, Sheikh Sarwar, Huiyuan Chen, Yuan Chen, Changhe Yuan, Haiyang Zhang, Qilin Qi

llms agents reinforcement-learning fine-tuning

Related stories

UP-NRPA: User Portrait based Nested Rollout Policy Adaptation for Planning with Large Language Models in Goal-oriented Dialogue Systems

$\Psi$-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues

LLM-Driven Personalities for Decision Making in Emergency Simulations

Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

Robust Critics: Defending LLMs Against Multi-Turn Attacks

Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation