AAAI

Human Cognitive Biases in Explanation-Based Interaction: The Case of Within and Between Session Order Effect

人类认知偏差在基于解释的交互中的体现：会话内与会话间顺序效应的案例

Dario Pesenti, Alessandro Bogani, Katya Tentori, Stefano Teso

机构 * CIMeC, University of Trento（CIMeC、特伦托大学） 纠错

AI总结 研究探讨了基于解释的交互学习中顺序效应的影响，发现其在会话内有显著影响，但不会对反馈质量造成重大问题。

Comments 18 pages, 10 figures, published at AAAI 2026

Flowing Backwards: Improving Normalizing Flows via Reverse Representation Alignment

反向流动：通过反向表征对齐改进规范化流

Yang Chen, Xiaowei Xu, Shuai Wang, Chenhui Zhu, Ruxue Wen, Xubin Li, Tiezheng Ge, Limin Wang

AI总结 通过反向表征对齐改进规范化流，提升生成质量和分类准确性，训练速度提升3.3倍，实现ImageNet新state-of-the-art结果。

Comments Accepted by AAAI 2026

TongUI: Internet-Scale Trajectories from Multimodal Web Tutorials for Generalized GUI Agents

TongUI: 通过多模态网络教程构建大规模GUI代理

Bofei Zhang, Zirui Shang, Zhi Gao, Wang Zhang, Rui Xie, Xiaojian Ma, Tao Yuan, Xinxiao Wu, Song-Chun Zhu, Qing Li

AI总结 TongUI通过多模态网络教程构建大规模GUI代理，利用GUI-Net数据集提升定位和导航性能，优于基线代理10%。

Comments AAAI 2026

Towards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective

迈向伦理化的大型语言模型多智能体系统：从机制可解释性视角出发

Jae Hee Lee, Anne Lauscher, Stefano V. Albrecht

AI总结 本文从机制可解释性视角出发，提出确保大型语言模型多智能体系统伦理行为的研究议程，聚焦于伦理评估框架、内部机制解析及参数高效对齐技术。

Comments Accepted to LaMAS 2026@AAAI'26 (https://sites.google.com/view/lamas2026)

Automating Complex Document Workflows via Stepwise and Rollback-Enabled Operation Orchestration

通过分步和回滚功能的操作编排自动化复杂文档工作流

Yanbin Zhang, Hanhui Ye, Yue Bai, Qiming Zhang, Liao Xiang, Wu Mianzhi, Renjun Hu

机构 * Yanbin Zhang, Hanhui Ye, Yue Bai, Qiming Zhang, Liao Xiang, Wu Mianzhi, Renjun Hu（作者） 纠错

AI总结 AutoDW通过分步和回滚功能的操作编排，提升复杂文档工作流的自动化效率

Comments 9 pages, 3 figures, accepted by AAAI-2026

Executable Governance for AI: Translating Policies into Rules Using LLMs

可执行治理 for AI：利用 LLMs 将政策翻译成规则

Gautam Varma Datla, Anudeep Vurity, Tejaswani Dash, Tazeem Ahmad, Mohd Adnan, Saima Rafi

AI总结 利用 LLMs 将 AI 政策转化为可执行规则，通过 P2T 框架实现标准化表示，提升政策执行的自动化与安全性。

Comments Accepted to AAAI-26 AI Governance Workshop (in-person presentation); 10 pages, 5 figures

Sharp Eyes and Memory for VideoLLMs: Information-Aware Visual Token Pruning for Efficient and Reliable VideoLLM Reasoning

锐眼与记忆：视频大语言模型的信息感知视觉标记修剪方法

Jialong Qin, Xin Zou, Di Lu, Yibo Yan, Xuming Hu

AI总结 SharpV通过自适应视觉标记修剪和键值缓存修剪，提升视频大语言模型的效率与可靠性，是首个无需暴露注意力分数的两阶段修剪框架。

Comments The 40th Annual AAAI Conference on Artificial Intelligence (AAAI-26) Poster

Physics-Informed Deformable Gaussian Splatting: Towards Unified Constitutive Laws for Time-Evolving Material Field

物理引导的可变形高斯点云：面向时间演化的材料场统一本构定律

Haoqin Hong, Ding Fan, Fubin Dou, Zhi-Li Zhou, Haoran Sun, Congcong Zhu, Jingrun Chen

AI总结 本文提出物理引导的可变形高斯点云方法，通过引入时间变化的本构参数和物理约束，提升动态场景的物理一致性和重建质量。

Comments Accepted by AAAI-26

Semi-Supervised Synthetic Data Generation with Fine-Grained Relevance Control for Short Video Search Relevance Modeling

半监督合成数据生成与细粒度相关性控制用于短视频搜索相关性建模

Haoran Li, Zhiming Su, Junyan Yao, Enwei Zhang, Yang Ji, Yan Chen, Kan Zhou, Chao Feng, Jiao Ran

AI总结 本文提出一种半监督合成数据生成方法，通过细粒度相关性控制提升短视频搜索相关性建模效果，实验显示其在推荐系统中提升了CTR、SRR和IUPR

Comments Submitted to AAAI 2026

CMMCoT: Enhancing Complex Multi-Image Comprehension via Multi-Modal Chain-of-Thought and Memory Augmentation

CMMCoT：通过多模态链式思考和记忆增强提升复杂多图像理解

Guanghao Zhang, Tao Zhong, Yan Xia, Mushui Liu, Zhelun Yu, Haoyuan Li, Wanggui He, Fangxun Shu, Dong She, Yi Wang, Hao Jiang

AI总结 CMMCoT通过多模态链式思考和记忆增强提升多图像理解的复杂性与准确性

Comments Accepted by AAAI 2026

Optimizing Product Provenance Verification using Data Valuation Methods

利用数据估值方法优化产品溯源验证

Raquib Bin Yousuf, Hoang Anh Just, Shengzhe Xu, Brian Mayer, Victor Deklerck, Jakub Truszkowski, John C. Simeone, Jade Saunders, Chang-Tien Lu, Ruoxi Jia, Naren Ramakrishnan

AI总结 本文提出了一种数据估值框架，通过Shapley值优化SIRA模型的训练数据选择，提升溯源验证的准确性和稳健性，实验证明其在实际应用中的有效性。

Comments Proceedings of the AAAI Conference on Artificial Intelligence 2026

Surface-Based Visibility-Guided Uncertainty for Continuous Active 3D Neural Reconstruction

基于表面的可见性引导不确定性用于连续主动3D神经重建

Hyunseo Kim, Hyeonseo Yang, Taekyung Kim, YoonSung Kim, Minsu Lee, Jin-Hwa Kim, Byoung-Tak Zhang

AI总结 本文提出基于表面的可见性场（SBV），用于连续主动3D神经重建中估计可见性引导的不确定性，通过学习渲染不确定性并更新表面置信度，提升重建性能达11.6%。

Comments The main claims are the same as in the previous version, but the naming and explanations have been changed. Accepted at AAAI 2026 Artificial Intelligence with Biased or Scarce Data workshop