arXiv AI July 17, 2026 By Boning Zhao, Yutong Hu, Xinnuo Li

From Stateless to Situated: Building a Psychological World for LLM-Based Agents

arXiv:2603. 25031v2 Announce Type: replace Abstract: In psychological support and emotional companionship scenarios, the core limitation of large language models (LLMs) lies not merely in response quality, but in their reliance on local next-token prediction, which prevents them from maintaining the temporal continuity, stage awareness, and user consent boundaries required for multi-turn intervention.

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

arXiv AI

Jun 17

EmoFSM: A Finite State Machine for Emotional Support Conversation

arXiv:2504. 11837v3 Announce Type: replace-cross Abstract: Emotional support conversation (ESC) aims to alleviate people's emotional distress through effective conversations.

By Yue Zhao, Qingqing Gu, Xiaoyu Wang, Teng Chen, Zhonglin Jiang, Yong Chen, Hongyan Li, Luo Ji

llms fine-tuning

arXiv AI

Aug 3

Cognitive World Model for Progressive BDI/E Trajectory Evaluation of Conversational Agents

arXiv:2606. 29495v2 Announce Type: replace Abstract: As LLM-based conversational agents advance toward increasingly open-ended and interaction-intensive scenarios, task completion alone provides an incomplete assessment of their effectiveness.

By Minghui Ma, Bin Guo, Hao Wang, Han Wang, Mengqi Chen, Jingqi Liu, Yan Liu

llms agents benchmarks

arXiv AI

Jun 16

State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs

arXiv:2606. 16307v1 Announce Type: new Abstract: Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings, and largely absent from public datasets.

By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Das, Jitesh Chandra, Yun-Shiuan Chuang, Chaitanya Kulkarni, Arun Menon, Linsey Pang, Avinash Karn, Mouli V, Prakhar Mehrotra

llms agents safety

arXiv AI

Jul 10

From Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue

arXiv:2607. 07824v1 Announce Type: cross Abstract: Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive storytelling.

By Jingyao Cai, Shuaijun Liu, Abdul Rehman, Yutong Guo, Qin Tian, Thomas Dolby, Sue Green, Chantel Cox, Xiaosong Yang

llms agents benchmarks

arXiv AI

Jul 28

ARdena: Scenario-driven control of real-time LLM agents

arXiv:2607. 22651v1 Announce Type: new Abstract: Large language models (LLMs) have enabled increasingly capable conversational agents, but reliably controlling their behavior in real-time interactive environments remains a significant challenge.

By Luka Borozan, Domagoj Matijevi\'c

llms agents robotics fine-tuning multimodal safety

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

llms agents reinforcement-learning fine-tuning benchmarks

Related stories

EmoFSM: A Finite State Machine for Emotional Support Conversation

Cognitive World Model for Progressive BDI/E Trajectory Evaluation of Conversational Agents

State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs

From Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue

ARdena: Scenario-driven control of real-time LLM agents

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