Mind2Report: Expert-Level Commercial Report Synthesis via Cognitive Deep Research Agent
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；Artificial Intelligence Engineering Institute, iFLYTEK Co., Ltd（人工智能工程院，iFLYTEK公司）
Synthesizing informative commercial reports from massive and noisy web sources is critical for high-stakes business decisions. Although recent deep research agents (DRAs) achieve notable progress, their reports remain limited in quality, reliability, and coverage. These mainly stem from ambiguous intents that cause search drift, retrieved web content that rapidly saturates the context window, and single-pass synthesis that limits report comprehensiveness. In this work, we propose Mind2Report, a cognitive deep research agent that emulates commercial analysts to synthesize expert-level reports. Mind2Report first probes fine-grained commercial intent to establish a structured outline, then recursively explores web sources and distills validated evidence into research memory to preserve context efficiency. Meanwhile, the research memory and outline continuously co-evolve, refining the report structure to avoid rigid initial planning. Finally, Mind2Report iteratively synthesizes the report based on the evolving outline and accumulated evidence. Together, these designs enable reliable and context-efficient long-horizon commercial deep research. To rigorously evaluate commercial DRAs, we further construct QRC-Eval, comprising 200 real-world commercial tasks and a holistic evaluation framework covering report quality, reliability, and coverage. Extensive experiments demonstrate that Mind2Report consistently outperforms leading proprietary and open-source DRAs, while ablations verify the effectiveness of each module and further analyze the challenges they address. We expect this work to advance the development of commercial deep research agents.
GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Institute for Advanced Algorithms Research, Shanghai（上海先进算法研究所）；The University of Manchester（曼彻斯特大学）；The University of Pittsburgh（匹兹堡大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Sydney（悉尼大学）
SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View
Sichuan University（四川大学）；Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education（教育部机器学习与工业智能工程研究中心）；School of Statistics and Data Science, AAIS, Nankai University（南开大学统计与数据科学学院）
Emulating Human Cognitive Processes for Expert-Level Medical Question-Answering with Large Language Models
Dell Medical School at the University of Texas（德克萨斯大学戴尔医学院）；William Beaumont University Hospital（威廉·博蒙特大学医院）；Austin State Hospital（奥斯汀州立医院）；Institute of Mental Health, University of Nottingham（诺丁汉大学精神卫生研究所）；Government Medical College and Hospital, Chandigarh（昌迪加尔政府医学院及医院）；University of Augsburg（奥格斯堡大学）；McGovern Medical School and The University of Texas Health Science Center（麦戈文医学院与德克萨斯大学健康科学中心）；Sheikh Shakhbout Medical City（谢赫·沙赫布特医疗城）；Dr Akbar Niazi Teaching Hospital（阿克巴尔·尼亚兹博士教学医院）；Centro Médico ABC（ABC医疗中心）；Houston Regional Gastroenterology Institute（休斯顿地区胃肠病学研究所）；NeuroReef Labs（NeuroReef实验室）
SciOrch: Learning to Orchestrate Expert LLMs for Solving Frontier Multimodal Scientific Reasoning Tasks
Imperial College London（伦敦帝国学院）；The Chinese University of Hong Kong（香港中文大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Oxford（牛津大学）；Shenzhen Loop Area Institute（深圳河套学院）
Étude cognitive des processus de construction d'une requête dans un système de gestion de connaissances médicales
Laboratoire LITIS - EA 4108（LITIS实验室 - EA 4108）；Laboratoire LiDiFra（LiDiFra实验室）
本文介绍了认知CISMeF项目，其目的是提高医疗信息搜索的CISMeF系统（法语健康资源的目录和索引），包括一个会话代理与用户进行交互的自然语言。为了研究信息搜索过程中涉及的认知过程，采用了自下而上的方法。实验已经建立，以获得人类之间的对话用户（扮演病人的角色）处理医疗信息搜索和CISMeF专家细化的要求。这些对话的分析强调使用的话语证据：词汇，重新制定，隐含或明确的表达用户意图，会话序列等人工代理的模型提出。它通过向用户提供示例、帮助和选择来引导用户进行信息搜索。该模型已在CISMeF系统中实现并集成。- Cet article décrit le projet Cogni-CISMeF qui proposed un module de dialogue Homme-Machine à intégrer dans le système d'independence de connaissances medicales CISMeF（Catalogue et Index des Sites Médicaux Francophones）.我们采用了一种认知改进的方法，通过一个使用者（jouant le rôle d'un patient）之间的对话语料库，提供医学信息和一名CISMeF专家，以满足这一需求。我们分析对话的结构是显而易见的，也研究了某些索引：vocabulaire e…
CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education
Universidade de Vigo（维戈大学）；IFCAE-Instituto de Investigación en Física, Computación y Ciencia Aeroespacial, Universidade de Vigo（IFCAE-维戈大学物理、计算与航空航天科学研究所）；Computer Science Department, IFCAE-Instituto de Investigación en Física, Computación y Ciencia Aeroespacial, Universidade de Vigo（维戈大学IFCAE物理、计算与航空航天科学研究所计算机科学系）；Universidade de Vigo, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática（维戈大学计算机科学系ESEI高等信息工程学院）
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
Universidade de Vigo, Department of Computer Science, ESEI-Higher School of Computer Engineering（维戈大学计算机科学系ESEI高等计算机工程学院）；IFCAE-Institute for Research in Physics, Computing and Aerospace Science（IFCAE物理、计算与航空航天科学研究所）
