We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by calculating the matching score based on learned features, leading to insufficient model reasoning ability. In this paper, we propose a graph-reasoning network (GRN) to address the problem. GRN first conducts pre-training based on ALBERT using next utterance prediction and utterance order prediction tasks specifically devised for response selection. These two customized pre-training tasks can endow our model with the ability of capturing semantical and chronological dependency between utterances. We then fine-tune the model on an integrated network with sequence reasoning and graph reasoning structures. The sequence reasoning module conducts inference based on the highly summarized context vector of utterance-response pairs from the global perspective. The graph reasoning module conducts the reasoning on the utterance-level graph neural network from the local perspective. Experiments on two conversational reasoning datasets show that our model can dramatically outperform the strong baseline methods and can achieve performance which is close to human-level.
HiDiffTIR: Hierarchical Difficulty-Aware Policy Optimization for Multi-Turn Tool-Integrated Reasoning
State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Meituan（美团）
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
University of Oxford（牛津大学）；VulpiVox Intelligence；Eindhoven University of Technology（埃因霍温理工大学）；Griffith University（格里菲斯大学）；Squirrel Ai Learning；East China Normal University（华东师范大学）
VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents
Northwestern University（西北大学）；University of Washington（华盛顿大学）；Stanford University（斯坦福大学）；Microsoft（微软）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能方向）；Didichuxing Co. Ltd（滴滴出行有限公司）；CSE, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）
Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases
Center for Biomedical Data Science, Duke-NUS Medical School（生物医学数据科学中心，杜克 - 新加坡国立大学医学院）；Duke-NUS AI + Medical Sciences Initiative, Duke-NUS Medical School（杜克 - 新加坡国立大学人工智能与医学科学计划，杜克 - 新加坡国立大学医学院）；Department of Population Health Sciences, Weill Cornell Medicine（人口健康科学系，威尔康奈尔医学院）；System Engineering, College of Engineering, Cornell University（系统工程，康奈尔大学工程学院）；Graduate School of Frontier Sciences, The University of Tokyo（前沿科学研究生院，东京大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Department of Biostatistics and Bioinformatics, Duke University（生物统计学与生物信息学系，杜克大学）；Perelman School of Medicine, University of Pennsylvania（佩雷尔曼医学院，宾夕法尼亚大学）；School of Clinical Medicine, University of Cambridge（临床医学学院，剑桥大学）；Hospital of the University of Pennsylvania（宾夕法尼亚大学医院）；Children’s Hospital of Philadelphia (CHOP)（费城儿童医院）；Department of Anatomic Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania（解剖病理学与检验医学系，宾夕法尼亚大学医院）；Department of Pathology and Laboratory Medicine, University of California, San Francisco（病理学与检验医学系，加州大学旧金山分校）；Language Technologies Institute, Carnegie Mellon University（语言技术研究所，卡内基梅隆大学）
IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；yz-intelligence Inc（元智智能公司）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning
National University of Singapore（新加坡国立大学）；Northeastern University（东北大学）；Sea AI Lab（Sea AI 实验室）；Centre for Frontier AI Research (CFAR), A*STAR（前沿人工智能研究中心（CFAR），A*STAR）；Plastic Labs；University of Washington（华盛顿大学）
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning
Beijing University of Posts and Telecommunications（北京邮电大学）；Data Science Lab, JD.com（京东数据科学实验室）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）
