AAAI

Vehicle Routing with Finite Time Horizon using Deep Reinforcement Learning with Improved Network Embedding

具有有限时间跨度的车辆路径问题使用改进的网络嵌入深度强化学习

Ayan Maity, Sudeshna Sarkar

AI总结 本文提出了一种改进的网络嵌入深度强化学习方法，用于解决具有有限时间跨度的车辆路径问题，通过结合局部节点嵌入和全局图表示，提升了客户服务水平和求解效率。

Comments Accepted at AAAI-26 Workshop on AI for Urban Planning

Pb4U-GNet: Resolution-Adaptive Garment Simulation via Propagation-before-Update Graph Network

Pb4U-GNet：通过传播前更新图网络实现分辨率自适应的服装模拟

Aoran Liu, Kun Hu, Clinton Ansun Mo, Qiuxia Wu, Wenxiong Kang, Zhiyong Wang

AI总结 Pb4U-GNet通过动态传播深度控制和几何感知更新缩放，实现了在不同分辨率网格上的自适应服装模拟。

Comments Camera-ready version accepted at AAAI 2026

LiViBench: An Omnimodal Benchmark for Interactive Livestream Video Understanding

LiViBench：面向交互式直播视频理解的多模态基准测试

Xiaodong Wang, Langling Huang, Zhirong Wu, Xu Zhao, Teng Xu, Xuhong Xia, Peixi Peng

AI总结 LiViBench是首个面向交互式直播视频的多模态基准测试，通过定制化两阶段指令微调和视频到评论检索模块，提升模型对直播视频的理解能力，并在多个基准测试中取得优异成绩。

Comments AAAI 2026 Main Track

Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness

具有图引导噪声意识的量子程序适应保真度估计

Tingting Li, Ziming Zhao, Jianwei Yin

AI总结 QuFid通过图引导噪声意识框架，实现量子程序的自适应保真度估计，有效降低测量成本并保持保真度精度。

Comments Published in AAAI 2026;

SOSControl: Enhancing Human Motion Generation through Saliency-Aware Symbolic Orientation and Timing Control

SOSControl: 通过具有显著性意识的符号性方向和时间控制增强人体运动生成

Ho Yin Au, Junkun Jiang, Jie Chen

AI总结 SOSControl通过显著性意识的符号性方向和时间控制提升人体运动生成的质量与可控性。

Comments Accepted by AAAI 2026

Does Less Hallucination Mean Less Creativity? An Empirical Investigation in LLMs

减少幻觉是否意味着减少创造力？在大语言模型中的实证研究

Mohor Banerjee, Nadya Yuki Wangsajaya, Syed Ali Redha Alsagoff, Min Sen Tan, Zachary Choy Kit Chun, Alvin Chan Guo Wei

AI总结 本研究探讨了三种减少幻觉的方法对大语言模型创造力的影响，发现CoVe促进发散思维，DoLa抑制创造力，RAG影响较小，为科学应用中的准确性与创造性平衡提供指导。

Comments Accepted at the AAAI 2026 Workshop on AI for Scientific Research (AI4Research)

Beyond Boundaries: Leveraging Vision Foundation Models for Source-Free Object Detection

超越边界：利用视觉基础模型进行无源目标检测

Huizai Yao, Sicheng Zhao, Pengteng Li, Yi Cui, Shuo Lu, Weiyu Guo, Yunfan Lu, Yijie Xu, Hui Xiong

AI总结 本文提出一种利用视觉基础模型提升无源目标检测性能的新框架，通过增强特征对齐和标签质量，实现跨领域迁移和辨别性提升。

Comments Accepted to AAAI 2026. Extended version with full Appendix

Explaining Tournament Solutions with Minimal Supports

用最小支持解释竞赛解

Clément Contet, Umberto Grandi, Jérôme Mengin

AI总结 本文研究了如何通过最小支持来解释竞赛解，提出了多项式时间算法计算各竞赛规则下的最小支持，并展示了其在生成认证解释中的应用。

Comments This paper is the extended version of Contet, Grandi, and Mengin. 2026. Explaining Tournament Solutions with Minimal Supports. In Proceedings of the 40th AAAI Conference on Artificial Intelligence

BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling

BayesAgent: 通过 verbalized 概率图模型实现不确定性下的贝叶斯代理推理

Hengguan Huang, Xing Shen, Songtao Wang, Lingfa Meng, Dianbo Liu, David Alejandro Duchene, Hao Wang, Samir Bhatt

AI总结 BayesAgent 通过 verbalized 概率图模型实现不确定性下的贝叶斯代理推理，提升置信度校准和文本生成质量。

Comments Accepted to AAAI 2026