Multi-Turn Dialogue Management

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36 papers12 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Ontology-Driven Control of Conversational Agents with a Use-Case for Job Interview Practice
Nov 6, 2026Barbara Gendron, Gaël Guibon, Mathieu D'aquinConversational Agents LLM Agent Training
LORIA, UL · Université de Lorraine, CNRS, LORIA, Nancy France · LIPN, LORIA +1
Recursive Self-Improvement for Long-Horizon Role-Playing Dialogue
Nov 5, 2026Y. L. Li, Y. H. WuLarge Language Model-Based Role-Play Simulation Recursive Self-Improvement
Henan Chengxi Technology Co., Ltd.
YANAMI: A Large-Scale Multimodal Dataset and Benchmark for Multiturn Stateful Interaction
Nov 3, 2026Shengyu Wang, Tengyue Nong, Junming Liu +10Multimodal Dataset Multi-Turn Dialogue Evaluation
The Hong Kong University of Science and Technology · Independent Researcher · The University of Melbourne +7
Don't Guess, Ask Wisely: Benchmarking and Training Proactive Elicitation in Open-Domain LLM Conversations
Nov 1, 2026Rongyu Zhang, Mutian Bao, Jinjian Zhang +8Clarification Question Generation Multi-Turn Dialogue Management
Zhejiang University · Ant Group · City University of Hong Kong
Can Large Language Models Extend Their Agentic Capabilities to Goal Pursuit in Open-Ended Multi-Turn Interaction?
Nov 1, 2026Mingyu Chen, Jingkai Lin, Xiaofen Xing +3Tool-Using Agents Multi-Turn Dialogue Management
School of EE., South China University of Technology, Guangzhou, China · Foshan University, Foshan, China
Accounting for Irregular State Transitions through Belief Estimation in Online Medical Consultation
Nov 1, 2026Mingzhe Lu, Xiangru Chen, Jian Qin +2Multi-Turn Dialogue Management Healthcare
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · Culverhouse College of Business, The University of Alabama, Tuscaloosa, AL, USA +1
Agentic Context Management for Multi-Turn Human-AI Conversations
Oct 31, 2026Matthew Ho, Chenglong Wang, Baolin Peng +5LLM Context Management Multi-Turn Dialogue Management
University of California, San Diego · Microsoft Research
StateWise: Teaching Language Models to Track and Obey Evolving Constraints in Multi-Turn Dialogue
Oct 24, 2026Samarth Varshney, Ritu YadavInstruction Following Multi-Turn Dialogue Management
CounselPlex: A Stressor-Aware Full-Duplex Counseling Model
Oct 23, 2026Yeonju Kim, Bella Godiva, Yong Man RoConversational Agents Full-Duplex Spoken Dialogue Systems
Integrated Vision and Language Lab, KAIST, South Korea
Beyond What to Say: Proactive Utterance Timing with Inner Thoughts for Werewolf Agents
Oct 23, 2026Yoshiki Tanaka, Shuya Uchiyama, Daichi Hamaguchi +1Proactive Agents Game-Playing Agents
The University of Electro-Communications, Tokyo, Japan
Dialogue Generation for Conversational Recommendation Using User Search Interactions and Knowledge Graphs
Oct 18, 2026Minsung Hwang, Donggyu Yoon, Gowoon Hwang +2Text Generation Recommender Systems
School of Computing, KAIST Daejeon, Republic of Korea · LG Electronics Seoul, Republic of Korea · Department of AI Computing, KAIST Daejeon, Republic of Korea
Jarvis: Coordination Deserves a Brain of Its Own in Agentic Interactive Models
Oct 16, 2026Yuxiang Wang, Shengbo Cai, Zhixian Zhao +7LLM Agent Harnesses Multi-Agent Coordination
The Chinese University of Hong Kong, Shenzhen · Tencent Hunyuan · Amphion Technology Co., Ltd.
RePlay: Retrieval-Based Voice Playback for Multi-Turn spoken dialogue
Sep 25, 2026Sathvik Udupa, Naveen Kumar, Ryan FolmsbeeSpoken Dialogue Systems Multi-Turn Dialogue Management
Disney Research Imagineering, Los Angeles, USA
Pull: Lazy Materialization of Working Memory for Stateful LLM Conversations
Sep 13, 2026Jiangang ChenLLM Inference Efficiency LLM Memory
Chengdu Beiluoshimen Technology Co., Ltd.
Acquire, Repair, Preserve: A Diagnosis-Guided Post-Training Recipe for Small-Model Dialogue Game Agents
Aug 28, 2026Nan LiPreference Learning for LLMs LLM Agent Training
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems
Aug 28, 2026Hanglong Lv, Dawei Zhu, Lei Li +11User Simulation LLM Agent Training
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · LLM-Core, Xiaomi · The University of Hong Kong +1
Towards Better Agents for Multi-Turn User Interaction: The Next User Turn Is More Than Context
Aug 18, 2026Yiwen Zhao, Zhihao Wen, Yuchen Mao +5Multi-Turn Dialogue Management RL for LLM Agents
Fudan University · Ant International, Ant Group
Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation
Aug 16, 2026Cedar Site Bai, Zhenyu Liao, Duanshun Li +6Information Gain Recommender Systems
Amazon · Sunnyvale, CA, USA · Seattle, WA, USA +1
Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding
Jul 31, 2026Eileen Ye, Jiawen Tao, Yaoming Li +7Conversational Memory LLM Evaluation
Hunyuan Team, Tencent · Peking University
Enjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging
Jul 11, 2026Jinglan Gong, Jiefan Lu, Hewei Guo +5Language Model Generation Evaluation Persona Consistency
SenseTime Research · University of Science and Technology of China · Tsinghua University
From Task-Guided Conversational Graphs to Goal-Oriented Dialogue Runtimes
Jun 22, 2026Mariano Garralda-BarrioLLM Agent Orchestration Multi-Turn Dialogue Management
Independent Researcher · Lleida, Spain
DynSess: Dynamic Session-Level Evaluation and Optimization Framework for Role-Playing Agents
May 28, 2026Rongsheng Zhang, Jiji Tang, Junnan Ren +6Large Language Model-Based Role-Play Simulation Long-Horizon Agent Evaluation
1Zhejiang University · 2Fuxi AI Lab, NetEase Inc. · 3Xiamen University
From Static Context to Calibrated Interactive RL: Mitigating Distribution Shift in Multi-turn Dialogue with Aligned Simulator
May 26, 2026Xiaohua Wang, Jiakang Yuan, Zisu Huang +5RL for Language Models Reinforcement Learning
Fudan University
Towards Multi-Turn Dialog Systems for Industrial Asset Operations and Maintenance
May 24, 2026Chengrui Li, Rujing Li, Yitong Bai +1Multi-Agent Orchestration Multi-Turn Dialogue Management
Columbia University
Found in Conversation: LLMs Teach Themselves to Close the Multi-Turn Gap
May 23, 2026Tianlang Chen, Shirley Wu, Jure LeskovecLanguage Model Distillation Teacher-Student Learning
Stanford
Unlocking Proactivity in Task-Oriented Dialogue
May 21, 2026Azure Zhang, Ning Gao, Yuqin Dai +7LLM Post-Training Conversational Agents
1Keeta AI, Meituan · 2Independent Researcher · 3CUHK +1
ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking
May 18, 2026Yanjun Lin, Zimo Xiao, Kartik Natarajan +7Spoken Dialogue Systems LLM Agents
Amazon
Improving Multi-turn Dialogue Consistency with Self-Recall Thinking
May 14, 2026Renning Pang, Tian Lan, Leyuan Liu +3Memory-Augmented Language Models Long-Term Conversational Memory
University of Electronic Science and Technology of China
SOMA: Efficient Multi-turn LLM Serving via Small Language Model
May 11, 2026Xueqi Cheng, Qiong Wu, Zhengyi Zhou +3LLM Serving Small Language Models
Florida State University · AT&T Chief Data Office · Louisiana State University +1
Beyond Continuity: Challenges of Context Switching in Multi-Turn Dialogue with LLMs
May 10, 2026Aditya Sinha, Harald Steck, Vito Ostuni +1LLM Evaluation Multi-Turn Dialogue Evaluation
Netflix Inc. · Los Gatos, CA, USA

Nov 6, 2026J/K move · Enter open · S save

Ontology-Driven Control of Conversational Agents with a Use-Case for Job Interview Practice

Barbara Gendron, Gaël Guibon, Mathieu D'aquin

LORIA, UL · Université de Lorraine, CNRS, LORIA, Nancy France · LIPN, LORIA+1

Conversational Agents LLM Agent Training Multi-Turn Dialogue Management

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Large Language Models (LLMs) have been widely used as conversational agents in both domain-specific and open-domain situations. In domain-specific context, it is often necessary to adapt those LLMs to obtain conversations with desired characteristics, whether those apply at the level of each response (e.g. for tone or style) or more globally (i.e. at the level of a conversational strategy). Such adaptations are, however, generally realized in an ad hoc way, through specific, non-reusable, and non-transparent developments. In this paper, we propose an ontology-based approach to design conversational strategies that rely on the desired and measured characteristics of textual utterances in the conversation. Through a use case related to job interview practice, we show how this approach can be used to control LLMs to follow the strategy defined in the ontology. To this end, we automatically fine-tune the LLM to improve its compliance with ontologically-defined conversation characteristics. Our results show that training the LLM from formally defined concepts effectively enhances alignment with the requested characteristics. Hence, this work demonstrates how ontology-guided conversation control can be achieved while taking into account multiple, concurrent conversation aspects that are formally and transparently defined.