Large Language Model (LLM)-driven conversational search is shifting information retrieval from reactive keyword matching to proactive, open-ended dialogues. In this context, Conversation Starters are widely deployed to provide personalized query recommendations that help users initiate dialogues. Conventionally, recommending these starters relies on a closed "exposure-click" loop. Yet, this feedback loop mechanism traps the system in an echo chamber where, compounded by data sparsity, it fails to capture the dynamic nature of conversational search intents shaped by the open world. As a result, the system skews towards popular but generic suggestions. In this work, we uncover an untapped paradigm shift to shatter this harmful feedback loop: harnessing user "free will" through active user expressions. Unlike traditional recommendations, conversational search empowers users to bypass menus entirely through manually typed queries. The open-world intents in active queries hold the key to breaking this loop. However, incorporating them is non-trivial: (1) there exists an inherent distribution shift between active queries and formulated starters. (2) Furthermore, the "non-ID-able" nature of open text renders traditional item-based popularity statistics ineffective for large-scale industrial streaming training. To this end, we propose Passive-Active Bridge (PA-Bridge), a novel framework that employs an adversarial distribution aligner to bridge the distributional gap between passively recommended starters and active expressions. Moreover, we introduce a semantic discretizer to enable the deployment of popularity debiasing algorithms. Online A/B tests on our platform, demonstrate that PA-Bridge significantly boosts the Feature Penetration Rate by 0.54% and User Active Days by 0.04%.
Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space
Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；Institute for Infocomm Research (I2R), A*STAR, Singapore（新加坡A*STAR信息与通信研究所）；Centre for Frontier AI Research (CFAR), A*STAR, Singapore（新加坡A*STAR前沿人工智能研究中心）
Old Habits Die Hard: How Conversational History Geometrically Traps LLMs
Technion - Israel Institute of Technology（技术ion-以色列理工学院）；University of Oxford（牛津大学）；University of Zagreb, FER（Zagreb大学，FER）；Kempner Institute, Harvard University（Kempner研究所，哈佛大学）；University of Edinburgh（爱丁堡大学）
Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment
Vrije University of Amsterdam（阿姆斯特丹自由大学）；University College London（伦敦大学学院）；University of Amsterdam（阿姆斯特丹大学）；National Institute of Informatics（日本信息处理学会）；Shandong University（山东大学）；Guangzhou University（广州大学）
DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation
School of Data Science, Fudan University（复旦大学数据科学学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；University of Toronto（多伦多大学）；School of Computer Science, Fudan University（复旦大学计算机学院）；Research Institute of Automatic and Complex Systems, Fudan University（复旦大学自动与复杂系统研究院）
Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs
Unbabel；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico & Universidade de Lisboa (Lisbon ELLIS Unit)（里斯本大学技术学院及里斯本大学（里斯本ELLIS单位））；MICS, CentraleSupélec, Université Paris-Saclay（MICS，CentraleSupélec，巴黎萨克雷大学）
Psychologically-Grounded Graph Modeling for Interpretable Depression Detection
Singapore Institute of Technology（新加坡科技学院）；Nanyang Technological University（南洋理工大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；University of Turku（图尔库大学）；Princess Nourah bint Abdulrahman University（普里西拉努拉·本·阿卜杜勒拉赫曼大学）
CrossWeave: Bridging Perspectives Across Online Communities with a Dual-Pane Design
Johns Hopkins University（约翰斯·霍普金斯大学）；Princeton University（普林斯顿大学）；Georgia Institute of Technology（佐治亚理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Northwestern University（西北大学）
