The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Caltech（加州理工学院）；AICrowd Research（AICrowd研究院）；Freie Universität Berlin（柏林自由大学）；OPPO Research Institute（OPPO研究院）；Northwestern University（西北大学）
Jennifer J. Sun, Tomomi Karigo, Dipam Chakraborty, Sharada P. Mohanty, Benjamin Wild, Quan Sun, Chen Chen, David J. Anderson, Pietro Perona, Yisong Yue, Ann Kennedy
Multi-agent behavior modeling aims to understand the interactions that occur between agents. We present a multi-agent dataset from behavioral neuroscience, the Caltech Mouse Social Interactions (CalMS21) Dataset. Our dataset consists of trajectory data of social interactions, recorded from videos of freely behaving mice in a standard resident-intruder assay. To help accelerate behavioral studies, the CalMS21 dataset provides benchmarks to evaluate the performance of automated behavior classification methods in three settings: (1) for training on large behavioral datasets all annotated by a single annotator, (2) for style transfer to learn inter-annotator differences in behavior definitions, and (3) for learning of new behaviors of interest given limited training data. The dataset consists of 6 million frames of unlabeled tracked poses of interacting mice, as well as over 1 million frames with tracked poses and corresponding frame-level behavior annotations. The challenge of our dataset is to be able to classify behaviors accurately using both labeled and unlabeled tracking data, as well as being able to generalize to new settings.
Inter-Stance: A Dyadic Multimodal Corpus for Conversational Stance Analysis
State University of New York at Binghamton（纽约州立大学布法罗分校）；Choate Rosemary Hall（乔斯-罗丝玛丽高中）；Rensselaer Polytechnic Institute（拉特格斯理工学院）；Ward Melville High School（沃德梅尔维尔高中）；University of Pittsburgh（匹兹堡大学）
Detection and Tracking of Multiple Mice Using Part Proposal Networks
Lancaster University（兰卡斯特大学）；University of Leicester（莱斯特大学）；Xidian University（西安电子科技大学）；Shenzhen NS-Tech Co.Ltd.（深圳新思科技有限公司）；Queen’s University Belfast（贝尔法斯特女王大学）；University of California at Merced（加州大学默塞德分校）
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?
JARA-Brain Institute II（JARA脑科学第二研究所）；RWTH Aachen University（亚琛工业大学）；Research Centre Juelich（于利希研究中心）；University Medical Center Göttingen（哥廷根大学医学中心）；Leibniz ScienceCampus Primate Cognition（莱布尼茨灵长类认知科学园区）
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency
Research Center on Information and Communication Technologies (CITIC)（信息与通信技术研究中心（CITIC））；Universidade da Coruña（拉科鲁尼亚大学）
有许多上下文中存在二元数据。社交网络就是一个众所周知的例子。在这些上下文中，成对的元素被链接起来，建立一个反映相互作用的网络。解释为什么建立这些关系对于获得透明度至关重要，透明度是一个越来越重要的概念。由于自然语言理解任务的普及，这些解释通常使用文本来呈现。我们的目标是代表和解释由任何代理建立的对（例如，推荐系统或付费促销机制），从而考虑基于文本的个性化。我们专注于TripAdvisor平台，考虑其他二元数据上下文的适用性。这些项目是用户和餐馆的子集，以及这些用户发布的评论的交互。我们提出了PTER（Personalised Text Based Reviews）模型。我们从给定餐厅的可用评论中预测适合特定用户交互的评论。PTER利用BERT（来自Transformers的双向编码器表示）变换器-编码器模型。我们根据基于特征的方法定制了一个深度神经网络，提出了一个LTR（学习排序）下游任务。我们进行了几次比较，我们的建议与随机基线和其他模型的最先进的，以下的额外（EXplanaTion Ranking）基准。我们的方法优于其他协同过滤建议。
AI Pedagogy: Dialogic Social Learning for Artificial Agents
University of Milan-Bicocca（米兰-比科卡大学）；Technical University of Munich（慕尼黑技术大学）；Institute for Cognitive Sciences and Technologies (ISTC-CNR)（认知科学与技术研究所（ISTC-CNR））
受维果茨基社会文化理论启发，本研究提出AI Social Gym动态环境，通过AI师生智能体间的二元教学对话进行本体获取，证明混合方向交互显著提升LLM知识获取与应用能力，为AI训练提供补充路径。
