Reasoning and question answering as a basic cognitive function for humans, is nevertheless a great challenge for current artificial intelligence. Although the Differentiable Neural Computer (DNC) model could solve such problems to a certain extent, the development is still limited by its high algorithm complexity, slow convergence speed, and poor test robustness. Inspired by the learning and memory mechanism of the brain, this paper proposed a Memory Transformation based Differentiable Neural Computer (MT-DNC) model. MT-DNC incorporates working memory and long-term memory into DNC, and realizes the autonomous transformation of acquired experience between working memory and long-term memory, thereby helping to effectively extract acquired knowledge to improve reasoning ability. Experimental results on bAbI question answering task demonstrated that our proposed method achieves superior performance and faster convergence speed compared to other existing DNN and DNC models. Ablation studies also indicated that the memory transformation from working memory to long-term memory plays essential role in improving the robustness and stability of reasoning. This work explores how brain-inspired memory transformation can be integrated and applied to complex intelligent dialogue and reasoning systems.
NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
University of Macau（澳门大学）；University of Chinese Academy of Sciences（中国科学院大学）；The Australian National University（澳大利亚国立大学）；Juntendo University（静冈大学）；Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Central South University（中南大学）；Southeast University（东南大学）；China Agricultural University（中国农业大学）；Adelaide University（阿德莱德大学）
UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation
Fung Institute for Engineering Leadership（工程领导力基金会）；University of California, Berkeley（加州大学伯克利分校）；Department of Radiology（放射科）；University of California, San Francisco（加州大学旧金山分校）；Division of Clinical Informatics and Digital Transformation（临床信息学与数字转型部）；Department of Neurological Surgery（神经外科部）
Talking to the brain: Using Large Language Models as Proxies to Model Brain Semantic Representation
South China Normal University（华南师范大学）；Center for Studies of Psychological Application, South China Normal University（华南师范大学心理应用研究中心）；School of Psychology, South China Normal University（华南师范大学心理学院）；Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University（华南师范大学广东省心理健康与认知科学重点实验室）
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph
Department of Computer Science and Technology, BNRist, Tsinghua University（清华大学计算机科学与技术系、BNRist）；Cloud BU, Huawei Technologies（华为技术有限公司云BU）；Nanyang Technological University（南洋理工大学）
多跳问答（QA）是一个具有挑战性的任务，因为它需要在每一步的答案与实体关系的精确推理。关系可以根据知识图谱中的标签来表示（例如，\textit{spouse}）或文本语料库中的文本（例如，\textit{they have been married for 26 years}）。现有的模型通常通过预测序列关系路径或聚集隐藏图特征来推断答案。前者很难优化，后者缺乏可解释性。在本文中，我们提出了TransferNet，一个有效的和透明的多跳QA模型，它支持在一个统一的框架中的标签和文本关系。TransferNet以多个步骤跨越实体。在每一步中，它关注问题的不同部分，计算关系的激活分数，然后以可微的方式沿着激活的关系传递先前的实体分数。我们在三个数据集上进行了广泛的实验，并证明了TransferNet在很大程度上超越了最先进的模型。特别是在MetaQA上，它在2跳和3跳问题中达到了100%的准确率。通过定性分析，我们表明，TransferNet具有透明和可解释的中间结果。
Actions and Objects Pathways for Domain Adaptation in Video Question Answering
Center for ICT and Automotive Convergence, Kyungpook National University（庆北国立大学信息通信技术与汽车融合中心）；Graduate School of Artificial Intelligence, Kyungpook National University（庆北国立大学人工智能研究生院）
University of Macau（澳门大学）；Xiamen University（厦门大学）；Peking University（北京大学）；Hanyang University（翰阳大学）；University of Liverpool（利物浦大学）；Zhejiang University（浙江大学）
A differentiable brain simulator bridging brain simulation and brain-inspired computing
School of Software Engineering, Beijing Jiaotong University（北京交通大学软件学院）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院）
Mind-Omni: A Unified Multi-Task Framework for Brain-Vision-Language Modeling via Discrete Diffusion
NeuBCI Lab, State Key Laboratory of Brain Cognition；Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China；School of Future Technology, University of Chinese Academy of Sciences, Beijing, China；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China；Zhongguancun Academy, Beijing, China；Peking University, Beijing, China
Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
National Center for Scientific Research “Demokritos”（德谟克利特国家科学研究中心）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Artificial Intelligence Research Institute（人工智能研究院）；HSE University（高等经济大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Center for Data Science, Peking University（北京大学数据科学中心）
OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks
Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen Memorial Hospital, Sun Yat-sen University（中山大学孙逸仙纪念医院）；Southern Medical University（南方医科大学）；Zhongshan Hospital, Fudan University（复旦大学中山医院）；Department of Neurosurgery, Prince of Wales Hospital（威尔士王室医院神经外科部）
Overview of BioASQ 2025: The Thirteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
Barcelona Supercomputing Center, Barcelona, Spain（巴塞罗那超级计算中心，巴塞罗那，西班牙）；Moscow State University, Russia（莫斯科国立大学，俄罗斯）；Artificial Intelligence Research Institute, Russia（人工智能研究 institute，俄罗斯）；Kazan Federal University, Russia（卡扎诺夫联邦大学，俄罗斯）；SberAI & Skoltech, Russia（SberAI 与 Skoltech，俄罗斯）；Aristotle University of Thessaloniki, Greece（亚里士多德大学，希腊）；Northwell Health, USA（Northwell Health，美国）；University of Padua, Italy（帕多瓦大学，意大利）；Archimedes, Athena Research Center, Greece（阿基米德，亚特兰蒂斯研究中心，希腊）
Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach
INRIA（法国国家信息与自动化研究所）；Universidad Nacional del Sur（南方国立大学）；Institute for Computer Science and Engineering (UNS–CONICET)（计算机科学与工程研究所（南方国立大学-国家科学技术研究委员会））；Universidad de Buenos Aires（布宜诺斯艾利斯大学）；Institute for Computer Science Research (UBA–CONICET)（计算机科学研究所（布宜诺斯艾利斯大学-国家科学技术研究委员会））
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Massachusetts Institute of Technology（麻省理工学院）；Shanghai Jiao Tong University（上海交通大学）；MIT（麻省理工学院）；UMass Amherst（马萨诸塞大学阿默斯特分校）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
