AAAI

Tractable Weighted First-Order Model Counting with Bounded Treewidth Binary Evidence

可计算的加权一阶模型计数与有界树宽二元证据

Václav Kůla, Qipeng Kuang, Yuyi Wang, Yuanhong Wang, Ondřej Kuželka

AI总结 本文提出了一种在有界树宽二元证据下计算加权一阶模型计数的多项式时间算法，并应用于解决稳定座位安排问题。

Comments To be published in AAAI 2026

Improving Region Representation Learning from Urban Imagery with Noisy Long-Caption Supervision

通过噪声长描述监督提升城市影像区域表示学习

Yimei Zhang, Guojiang Shen, Kaili Ning, Tongwei Ren, Xuebo Qiu, Mengmeng Wang, Xiangjie Kong

AI总结 本文提出UrbanLN框架，通过长文本意识和噪声抑制提升城市影像区域表示学习，有效解决细粒度特征对齐与噪声干扰问题。

Comments Accepted as a full paper by AAAI-26

DynaStride: Dynamic Stride Windowing with MMCoT for Instructional Multi-Scene Captioning

DynaStride: 基于MMCoT的动态步长窗多场景描述生成

Eddison Pham, Prisha Priyadarshini, Adrian Maliackel, Kanishk Bandi, Cristian Meo, Kevin Zhu

机构 * Algoverse AI Research（Algoverse人工智能研究） 纠错

AI总结 DynaStride通过多模态链式思考和动态步长窗算法，实现无需手动分割的多场景教学视频连贯描述生成，提升描述的连贯性和信息量。

Comments 16 pages, 15 figures, 5 Tables, Accepted at NeurIPS 7HVU Workshop, Accepted at AAAI AI4ED Workshop

Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting

Ziyu Zhou, Yiming Huang, Yanyun Wang, Yuankai Wu, James Kwok, Yuxuan Liang

Comments Accepted by AAAI 2026

WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural Network

WeightFlow: 通过神经网络权重的演变学习随机动力学

Ruikun Li, Jiazhen Liu, Huandong Wang, Qingmin Liao, Yong Li

AI总结 WeightFlow通过在神经网络权重空间中建模随机动力学，有效提升了高维随机过程建模的性能。

Comments Accepted by AAAI (Oral)

Multigranular Evaluation for Brain Visual Decoding

多粒度评估用于脑视觉解码

Weihao Xia, Cengiz Oztireli

AI总结 本研究提出BASIC框架，通过多粒度评估方法提升脑视觉解码的评估精度与解释性。

Comments AAAI 2026 (Oral). Code: https://github.com/weihaox/BASIC

GermanPartiesQA: Benchmarking Commercial Large Language Models and AI Companions for Political Alignment and Sycophancy

GermanPartiesQA: 对商业大语言模型和AI伴侣在政治倾向和趋炎附势方面的基准测试

Jan Batzner, Volker Stocker, Stefan Schmid, Gjergji Kasneci

机构 * TUM（慕尼黑大学） ； TUB（慕尼黑大学） 纠错

AI总结 GermanPartiesQA研究了六个商业LLMs在政治倾向和趋炎附势方面的表现，揭示了LLMs在生成事实性政党立场上的局限性以及模型特定的意识形态倾向模式。

Comments Published at AAAI/ACM AIES 2025. Presented at NeurIPS 2025 Workshop on LLM Evaluation and the International Monetary Fund's 12th Statistical Forum. GermanPartiesQA Benchmark under https://github.com/janbatzner/germanpartiesqa

Journal ref Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(1), 2025, pp. 330-342

GuideGen: A Text-Guided Framework for Paired Full-torso Anatomy and CT Volume Generation

GuideGen: 一种基于文本引导的完整躯干解剖和CT体积生成框架

Linrui Dai, Rongzhao Zhang, Yongrui Yu, Xiaofan Zhang

机构 * Shanghai Jiao Tong University（上海交通大学） 纠错

AI总结 GuideGen通过文本引导生成完整躯干解剖和CT体积，提升医学影像数据合成效率与质量。

Comments accepted as AAAI 2026 poster

mmPred: Radar-based Human Motion Prediction in the Dark

mmPred: 基于雷达的人体运动预测

Junqiao Fan, Haocong Rao, Jiarui Zhang, Jianfei Yang, Lihua Xie

AI总结 mmPred是首个基于雷达的扩散模型框架，通过双域表示和全局骨骼关系变换器提升雷达条件下的人体运动预测性能。

Comments This paper is accepted by AAAI-2026

Journal ref AAAI-2026

Measuring What LLMs Think They Do: SHAP Faithfulness and Deployability on Financial Tabular Classification

测量大语言模型认为它们能做什么：SHAP忠实性与金融表格分类中的部署性

Saeed AlMarri, Mathieu Ravaut, Kristof Juhasz, Gautier Marti, Hamdan Al Ahbabi, Ibrahim Elfadel

AI总结 研究评估了LLMs在金融表格分类中的SHAP值，发现其自我解释与SHAP值存在分歧，指出LLMs在结构化金融建模中的局限性，但认为改进的可解释性机制可提升其在风险敏感领域的应用潜力。

Comments 7 pages, 3 figures, 3 tables, AAAI 2026 Deployable AI Workshop

Causal Reinforcement Learning based Agent-Patient Interaction with Clinical Domain Knowledge

基于因果强化学习的Agent-患者交互与临床领域知识

Wenzheng Zhao, Ran Zhang, Ruth Palan Lopez, Shu-Fen Wung, Fengpei Yuan

AI总结 本文提出基于因果强化学习的Agent-患者交互框架，通过整合因果发现与推理提升医疗场景中的决策可解释性和适应性。

Comments Accepted by AAAI workshop

Look as You Think: Unifying Reasoning and Visual Evidence Attribution for Verifiable Document RAG via Reinforcement Learning

看而思：通过强化学习统一推理与视觉证据归因以实现可验证文档RAG

Shuochen Liu, Pengfei Luo, Chao Zhang, Yuhao Chen, Haotian Zhang, Qi Liu, Xin Kou, Tong Xu, Enhong Chen

AI总结 通过强化学习统一推理与视觉证据归因，提升多模态问答的可验证性和准确性。

Comments Poster of AAAI'2026

Cross Modal Fine-Grained Alignment via Granularity-Aware and Region-Uncertain Modeling

跨模态细粒度对齐 via 粒度感知和区域不确定建模

Jiale Liu, Haoming Zhou, Yishu Liu, Bingzhi Chen, Yuncheng Jiang

AI总结 本文提出了一种基于粒度感知和区域不确定建模的跨模态细粒度对齐方法，通过显著性感知和不确定性建模提升对齐的鲁棒性和可解释性。

Comments 10 pages, 6 figures, accepted by AAAI 2026

AHAMask: Reliable Task Specification for Large Audio Language Models without Instructions

AHAMask: 不依赖指令的大型音频语言模型可靠任务规范

Yiwei Guo, Bohan Li, Hankun Wang, Zhihan Li, Shuai Wang, Xie Chen, Kai Yu

AI总结 AHAMask通过掩码LALMs解码器中的注意力头，实现无需指令的可靠任务规范，实验表明其性能可媲美或超越指令方法。

Comments 15 pages, 10 tables, 6 figures. This is the camera ready version for AAAI 2026, plus an appendix for supplementary experimental details and results