The use of Large Language Models (LLMs) across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhance decision-making effectiveness with minimal human feedback. Concurrently, it seeks to align decisions with human expectations, preferences, and needs while mitigating risks associated with AI non-determinism. However, humans frequently over- or under-rely on AI recommendations, and current AI systems remain poorly calibrated to human expectations. To address these challenges, we introduce a human-AI collaborative decision-making framework designed to augment human capabilities and align AI agents with human preferences and expectations. Specifically, this paper (a) formulates the collaborative decision-making task as a stochastic game between an AI agent and a human player, and (b) proposes the Human-Centric Reflective Architecture (HCRA), which integrates human-calibrated models with reinforcement learning agents that leverage linguistic feedback in an iterative, reflective process. Evaluation results demonstrate that HCRA enhances decision-making effectiveness and delivers high-quality recommendations.
Can We Steer the Black-Box? Towards Controllability-Centric Evaluation of Recommender Systems with Collaborative Agents
Institute of Information Engineering, Chinese Academy of Sciences, China（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences, China（中国科学院大学网络安全学院）
Governing Reflective Human-AI Collaboration: A Framework for Epistemic Scaffolding and Traceable Reasoning
Faculty of Medicine, Lund University（Lund大学医学院）；Department of Economics, Lund University School of Economics and Management（Lund大学经济学与管理学院经济系）；Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine（伦敦卫生与热带医学学院健康服务研究与政策系）
Dynamic Framework for Collaborative Learning: Leveraging Advanced LLM with Adaptive Feedback Mechanisms
Swinburne University of Technology, Melbourne, Australia（斯winburne技术大学，墨尔本，澳大利亚）；Western Sydney University, Australia（西澳大学，澳大利亚）；Department of Computer Science, LUMS, Pakistan（计算机科学系，拉瓦尔大学，巴基斯坦）；UAE University, UAE（阿联酋大学，阿联酋）；Zayed University, UAE（扎耶德大学，阿联酋）；School of Business Western Sydney University, Australia（商学院，西澳大学，澳大利亚）
AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environment
Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）
