Constructing efficient and reliable policies to assist humans is indispensable for human-AI collaboration. Existing methods mainly follow two lines of work. Most prior work relies on multi-agent reinforcement learning (MARL) to learn black-box policies, which limits interpretability and raises safety concerns. Recent methods query large language models (LLMs) at each decision step, causing slow responses and high inference costs. We propose Collaboration Policy Tree (Co-pi-tree), a closed-loop method that learns an executable policy tree consisting of a partner-behavior prediction tree and an agent-action selection tree. Co-pi-tree constructs a policy by distilling LLM reasoning into policy tree code. It then evaluates the policy through partner interaction, obtains feedback, and uses natural language to summarize the interaction feedback to improve problematic branches. Experiments in Overcooked-AI show that Co-pi-tree improves average reward by 35.4% over the baseline average, while reducing the number of LLM queries by 77.7% and test-time latency by 97.1%. Project page: https://beiwenzhang.github.io/Co-pi-tree/
Distilling Deep Reinforcement Learning into Interpretable Fuzzy Rules: An Explainable AI Framework
Dept. of EECS, Syracuse University, NY 13244, USA（电子工程与计算机科学系， Syracuse大学，纽约州，13244，美国）；Air Force Research Laboratory, Rome, NY 13441, USA（空军研究实验室，罗马，纽约州，13441，美国）
Learn from A Rationalist: Distilling Intermediate Interpretable Rationales
Department of Computing Science, University of Alberta, Edmonton, Canada（阿尔伯塔大学计算机科学系，加拿大埃德蒙顿）；Alberta Machine Intelligence Institute, Edmonton, Canada（阿尔伯塔机器智能研究所，加拿大埃德蒙顿）
Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry
University of Michigan（密歇根大学）；UNC, Chapel Hill（北卡罗来纳大学教堂山分校）；Georgia Tech（佐治亚理工学院）；Apple（苹果公司）；Robert Bosch SRL（博世股份有限公司）；Babeş-Bolyai University（巴贝什-博耶亚大学）
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Harvard Medical School（哈佛医学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Southampton（南安普顿大学）；Athena Research Centre（雅典娜研究中心）
MedCoRAG: Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus
School of Cyber Science and Engineering, Nanjing University of Science and Technology（南京理工大学信息科学与工程学院）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Department of Neurology, China-Japan Union Hospital of Jilin University（吉林大学中日联谊医院神经内科）；Department of Anesthesiology, China-Japan Union Hospital of Jilin University（吉林大学中日联谊医院麻醉科）；School of Computing, Macquarie University（麦考瑞大学计算机学院）
Aetheria: A multimodal interpretable content safety framework based on multi-agent debate and collaboration
Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究院（TeleAI），中国电信）；Sichuan University（四川大学）；Peking University（北京大学）；Beijing Jiaotong University（北京交通大学）；Harbin Institute of Technology（哈尔滨工业大学）；China Railway Rolling Stock Corporation Limited（中国铁路滚动股票有限公司）
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
Grado Department of Industrial & Systems Engineering, Virginia Tech（弗吉尼亚理工大学格拉多工业与系统工程系）；Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；School of Mathematics, Physics, and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）；Center for Health Research, University of Southern Queensland（南昆士兰大学健康研究中心）
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching
Carnegie Mellon University（卡内基梅隆大学）；National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所（AIST））；University of Tsukuba（筑波大学）
From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration
Gamaizer；Université de technologie de Compiègne, CNRS, Heudiasyc（法国图尔学院、CNRS、Heudiasyc）；Sorbonne Université, CNRS UMR 7585, LPMHE（索邦大学、CNRS UMR 7585、LPMHE）
RTLSquad: Multi-Agent Based Interpretable RTL Design
National University of Defense Technology（国防科技大学）；Intelligent Microelectronics Center（智能微电子中心）；Qiyuan Lab（启元实验室）；Defense Innovation Institute（国防创新研究院）；Academy of Military Science（军事科学院）
Interpretable Machine Learning in Physics: A Review
University of Waterloo（滑铁卢大学）；Perimeter Institute for Theoretical Physics（周界理论物理研究所）；Homes Plus Magazine Inc.（Homes Plus杂志公司）；Santa Fe Institute（圣塔菲研究所）；ETH Zurich（苏黎世联邦理工学院）；University of Stuttgart（斯图加特大学）；Heidelberg Academy of Science and the Humanities（海德堡科学与人文学院）；Massachusetts Institute of Technology（麻省理工学院）
CoWork-X: Experience-Optimized Co-Evolution for Multi-Agent Collaboration System
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）科学与工程学院）；School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）人工智能学院）；Tsinghua University（清华大学）；AutoGame Research（AutoGame研究）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）
