AAAI

M3SR: Multi-Scale Multi-Perceptual Mamba for Efficient Spectral Reconstruction

M3SR：多尺度多感知Mamba用于高效光谱重建

Yuze Zhang, Lingjie Li, Qiuzhen Lin, Zhong Ming, Fei Yu, Victor C. M. Leung

AI总结 M3SR通过多尺度多感知架构提升超光谱图像重建的准确性和效率，同时降低计算成本。

Comments Accepted by AAAI 2026

A New Strategy for Verifying Reach-Avoid Specifications in Neural Feedback Systems

神经反馈系统中验证可达-回避规范的新策略

Samuel I. Akinwande, Sydney M. Katz, Mykel J. Kochenderfer, Clark Barrett

机构 * Department of Aeronautics and Astronautics, Stanford University, Stanford, USA（航空与航天系，斯坦福大学） ； Department of Computer Science, Stanford University, Stanford, USA（计算机科学系，斯坦福大学） 纠错

AI总结 本文提出了一种新的算法，用于计算神经反馈系统中可达-回避规范的上界和下界近似值，并结合前向分析技术，构建统一的验证框架。

Comments Accepted to AAAI-2026 Bridge Program B10: Making Embodied AI Reliable with Testing and Formal Verification

Transformer-Based Approach for Automated Functional Group Replacement in Chemical Compounds

基于变压器的方法用于化学化合物中的功能基团替换自动化

Bo Pan, Zhiping Zhang, Kevin Spiekermann, Tianchi Chen, Xiang Yu, Liying Zhang, Liang Zhao

AI总结 本文提出了一种基于变压器的两阶段模型，用于自动化化学化合物中功能基团的替换，通过序列生成确保子结构修改，有效捕捉转换规则并提升生成化学结构的多样性与有效性。

Comments The 2nd AAAI Workshop on Foundation Models for Biological Discoveries at AAAI 2025

Multiplicative Orthogonal Sequential Editing for Language Models

乘法正交序列编辑用于语言模型

Hao-Xiang Xu, Jun-Yu Ma, Ziqi Peng, Yuhao Sun, Zhen-Hua Ling, Jia-Chen Gu

AI总结 MOSE通过乘法正交矩阵编辑方法提升语言模型的序列编辑性能，同时保持一般能力。

Comments Accepted by AAAI 2026

Ground What You See: Hallucination-Resistant MLLMs via Caption Feedback, Diversity-Aware Sampling, and Conflict Regularization

在所见之地接地：通过标题反馈、多样性感知采样和冲突正则化实现抗幻觉的MLLMs

Miao Pan, Wangjie Gan, Jintao Chen, Wenqi Zhang, Bing Sun, Jianwei Yin, Xuhong Zhang

AI总结 本文提出抗幻觉的MLLMs方法，通过标题反馈、多样性感知采样和冲突正则化减少幻觉，提升推理准确性。

Comments AAAI-2026 Poster

Backdoor Attacks on Open Vocabulary Object Detectors via Multi-Modal Prompt Tuning

通过多模态提示调优对开放词汇目标检测器进行后门攻击

Ankita Raj, Chetan Arora

AI总结 TrAP通过多模态提示调优对开放词汇目标检测器实施后门攻击，利用轻量级提示标记植入恶意行为，提升攻击成功率并改进下游任务性能。

Comments Accepted to AAAI 2026

PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Efficient Super-Resolution of 4D Flow MRI

PINGS-X: 基于轴对齐的物理信息归一化高斯点云的4D流体MRI超分辨率方法

Sun Jo, Seok Young Hong, JinHyun Kim, Seungmin Kang, Ahjin Choi, Don-Gwan An, Simon Song, Je Hyeong Hong

AI总结 PINGS-X通过轴对齐高斯点云和归一化方法提升4D流体MRI超分辨率效率与精度。

Comments Accepted at AAAI 2026. Supplementary material included after references. 27 pages, 21 figures, 11 tables

SpatialActor: Exploring Disentangled Spatial Representations for Robust Robotic Manipulation

SpatialActor: 探索解耦的空间表示以实现鲁棒的机器人操作

Hao Shi, Bin Xie, Yingfei Liu, Yang Yue, Tiancai Wang, Haoqiang Fan, Xiangyu Zhang, Gao Huang

机构 * Dexmal 纠错

AI总结 SpatialActor通过解耦语义和几何，提升机器人操作的鲁棒性和精确性，实现高准确率和强泛化能力。

Comments AAAI 2026 Oral | Project Page: https://shihao1895.github.io/SpatialActor

Format as a Prior: Quantifying and Analyzing Bias in LLMs for Heterogeneous Data

以格式为先：量化和分析LLM在异构数据中的偏见

Jiacheng Liu, Mayi Xu, Qiankun Pi, Wenli Li, Ming Zhong, Yuanyuan Zhu, Mengchi Liu, Tieyun Qian

AI总结 本文通过三阶段分析，揭示了LLM在处理异构数据时的格式偏见问题，提出通过数据预处理、推理干预和平衡训练语料库来减少偏见的方法。

Comments Accepted by AAAI 2026, camera ready version

Interpreting Fedspeak with Confidence: A LLM-Based Uncertainty-Aware Framework Guided by Monetary Policy Transmission Paths

以信心解读Fedspeak：一种基于大语言模型的不确定性感知框架，由货币政策传导路径引导

Rui Yao, Qi Chai, Jinhai Yao, Siyuan Li, Junhao Chen, Qi Zhang, Hao Wang

AI总结 本文提出基于大语言模型的不确定性感知框架，用于解读Fedspeak并分类其货币政策立场，通过动态不确定性解码模块提升模型可靠性与准确性。

Comments Accepted by AAAI 2026 Oral

A Differential Perspective on Distributional Reinforcement Learning

分布强化学习的微分视角

Juan Sebastian Rojas, Chi-Guhn Lee

AI总结 本文从微分视角扩展分布强化学习至平均奖励设置，提出首个能有效学习长期每步奖励分布和微分回报分布的算法，并展示其在性能和信息捕捉方面的优势。

Comments In AAAI Conference on Artificial Intelligence 2026

Global Compression Commander: Plug-and-Play Inference Acceleration for High-Resolution Large Vision-Language Models

全局压缩指挥者：面向高分辨率大视觉-语言模型的即插即用推理加速

Xuyang Liu, Ziming Wang, Junjie Chen, Yuhang Han, Yingyao Wang, Jiale Yuan, Jun Song, Siteng Huang, Honggang Chen

机构 * Alibaba（阿里巴巴） 纠错

AI总结 本文提出GlobalCom²框架，通过全局缩略图引导局部裁剪压缩，实现高分辨率LVLMs的高效推理加速。

Comments Accepted by AAAI 2026. Code is available at \url{https://github.com/xuyang-liu16/GlobalCom2}

The Confidence Trap: Gender Bias and Predictive Certainty in LLMs

自信陷阱：大语言模型中的性别偏见与预测确定性

Ahmed Sabir, Markus Kängsepp, Rajesh Sharma

AI总结 本研究探讨大语言模型在性别偏见任务中的置信度校准问题，提出Gender-ECE指标以评估公平性差异，发现Gemma-2在性别偏见基准中校准最差。

Comments AAAI 2026 (AISI Track), Oral. Project page: https://bit.ly/4p8OKQD

Structural Approach to Guiding a Present-Biased Agent

结构化方法用于引导现时偏好代理

Tatiana Belova, Yuriy Dementiev, Artur Ignatiev, Danil Sagunov

AI总结 本文提出了一种结构化方法，用于引导现时偏好代理，通过参数化算法分析任务图的计算复杂性，并给出了固定参数可 tractable 算法。

Comments Accepted at AAAI 2026

Beyond Sharpness: A Flatness Decomposition Framework for Efficient Continual Learning

超越尖锐性：一种用于高效持续学习的平坦性分解框架

Yanan Chen, Tieliang Gong, Yunjiao Zhang, Wen Wen

AI总结 FLAD通过分解尖锐性感知扰动并保留噪声成分，提升持续学习的泛化能力与效率。

Comments Accepted by AAAI 2026

Task Prototype-Based Knowledge Retrieval for Multi-Task Learning from Partially Annotated Data

基于任务原型的知识检索多任务学习：从部分标注数据中学习

Youngmin Oh, Hyung-Il Kim, Jung Uk Kim

AI总结 本文提出基于原型的知识检索框架，通过任务原型嵌入和知识检索变压器实现稳健的多任务学习，适用于部分标注数据场景。

Comments Accepted at AAAI 2026

PulseMind: A Multi-Modal Medical Model for Real-World Clinical Diagnosis

PulseMind: 一种多模态医学模型用于真实世界临床诊断

Jiao Xu, Junwei Liu, Jiangwei Lao, Qi Zhu, Yunpeng Zhao, Congyun Jin, Shinan Liu, Zhihong Lu, Lihe Zhang, Xin Chen, Jian Wang, Ping Wang

机构 * Ant Group（蚂蚁集团） 纠错

AI总结 PulseMind通过整合多模态数据集和定制训练框架，提升真实世界临床诊断的准确性与实用性。

Comments Accepted to AAAI 2026

ActiShade: Activating Overshadowed Knowledge to Guide Multi-Hop Reasoning in Large Language Models

ActiShade: 通过激活被遮蔽的知识来指导大语言模型的多跳推理

Huipeng Ma, Luan Zhang, Dandan Song, Linmei Hu, Yuhang Tian, Jun Yang, Changzhi Zhou, Chenhao Li, Yizhou Jin, Xudong Li, Meng Lin, Mingxing Zhang, Shuhao Zhang

AI总结 ActiShade通过激活被遮蔽的知识来提升大语言模型在多跳推理中的表现。

Comments Accepted to AAAI 2026

Yes FLoReNce, I Will Do Better Next Time! Agentic Feedback Reasoning for Humorous Meme Detection

是的，Florne，我下次会做得更好！用于幽默表情包检测的代理反馈推理

Olivia Shanhong Liu, Pai Chet Ng, De Wen Soh, Konstantinos N. Plataniotis

AI总结 Florne通过闭环反馈机制提升表情包幽默检测的适应性和解释质量。

Comments LaMAS@AAAI 2026 (Oral)

Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAG

Relink：为GraphRAG构建查询驱动的证据图谱

Manzong Huang, Chenyang Bu, Yi He, Xingrui Zhuo, Xindong Wu

AI总结 Relink通过动态构建查询驱动的证据图谱，解决GraphRAG中知识图谱不完整和干扰事实的问题，提升问答性能。

Comments Accepted by AAAI 2026

MacPrompt: Maraconic-guided Jailbreak against Text-to-Image Models

MacPrompt：基于马卡罗尼的文本到图像模型对抗攻击

Xi Ye, Yiwen Liu, Lina Wang, Run Wang, Geying Yang, Yufei Hou, Jiayi Yu

AI总结 MacPrompt通过跨语言字符级重组有害术语，实现对文本到图像模型的安全机制的高效对抗，突破现有防御体系。

Comments Accepted by AAAI 2026

Explicit World Models for Reliable Human-Robot Collaboration

显式世界模型以实现可靠的人机协作

Kenneth Kwok, Basura Fernando, Qianli Xu, Vigneshwaran Subbaraju, Dongkyu Choi, Boon Kiat Quek

AI总结 本文提出通过构建显式世界模型来实现可靠的显式人机协作，强调动态模糊的人机交互需求。

Comments Accepted to AAAI-26 Bridge Program B10: Making Embodied AI Reliable with Testing and Formal Verification

REXO: Indoor Multi-View Radar Object Detection via 3D Bounding Box Diffusion

REXO：基于3D边界框扩散的多视角室内雷达目标检测

Ryoma Yataka, Pu Perry Wang, Petros Boufounos, Ryuhei Takahashi

机构 * MERL ； ITC 纠错

AI总结 REXO通过3D边界框扩散提升多视角室内雷达目标检测，利用先验知识减少参数并提升检测性能。

Comments 26 pages; Accepted to AAAI 2026; Code available at https://github.com/merlresearch/radar-bbox-diffusion

Intervention Efficiency and Perturbation Validation Framework: Capacity-Aware and Robust Clinical Model Selection under the Rashomon Effect

干预效率与扰动验证框架：在拉索莫恩效应下考虑容量的临床模型选择

Yuwen Zhang, Viet Tran, Paul Weng

AI总结 本文提出干预效率与扰动验证框架，用于在资源约束下稳健选择临床模型，解决拉索莫恩效应下的模型选择问题。

Comments Accepted to the Workshop on Navigating Model Uncertainty and the Rashomon Effect: From Theory and Tools to Applications and Impact (AAAI 2026)

Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network

利用孪生网络增强二进制编码的犯罪关联分析

Yicheng Zhan, Fahim Ahmed, Amy Burrell, Matthew J. Tonkin, Sarah Galambos, Jessica Woodhams, Dalal Alrajeh

AI总结 本文提出利用孪生网络增强二进制编码的犯罪关联分析，通过学习潜在表示和整合地理时间特征，提升关联准确性并提供可解释的见解。

Comments AAAI 2026, 7 pages, 4 figures

A Graph-Theoretical Perspective on Law Design for Multiagent Systems

多智能体系统的法律设计图论视角

Qi Shi, Pavel Naumov

AI总结 本文从图论角度研究多智能体系统中法律设计问题，提出两种法律类型并证明其最小化问题的计算复杂性，同时利用超图顶点覆盖的近似算法进行高效近似。

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

SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis

SpecDetect: 一种简单、快速且无需训练的LLM生成文本检测方法通过频谱分析

Haitong Luo, Weiyao Zhang, Suhang Wang, Wenji Zou, Chungang Lin, Xuying Meng, Yujun Zhang

AI总结 SpecDetect通过频谱分析技术，利用DFT总能量检测LLM生成文本，具有高效、快速且无需训练的特点。

Comments AAAI'26 Oral

GDBA Revisited: Unleashing the Power of Guided Local Search for Distributed Constraint Optimization

GDBA重审：释放指导局部搜索在分布式约束优化中的潜力

Yanchen Deng, Xinrun Wang, Bo An

AI总结 本文提出DGLS框架，通过改进约束惩罚机制和协调更新方案，提升分布式约束优化的性能。

Comments Accepted by AAAI 2026

Put the Space of LoRA Initialization to the Extreme to Preserve Pre-trained Knowledge

将LoRA初始化空间推向极端以保留预训练知识

Pengwei Tang, Xiaolin Hu, Yong Liu, Lizhong Ding, Dongjie Zhang, Xing Wu, Debing Zhang

机构 * Pengwei Tang 1,2,3（唐鹏伟） ； Xiaolin Hu 4（胡晓林） ； Yong Liu 1,2,3（刘勇） ； Lizhong Ding 5（丁立忠） ； Dongjie Zhang 6（张东杰） ； Xing Wu 6,7（吴星） ； Debing Zhang 6（张德兵） 纠错

AI总结 LoRA-Null通过在激活的空域中初始化LoRA，有效保留预训练知识并提升微调性能。

Comments Accepted at AAAI 2026. We rediscovered why our approach works from the perspective of the LoRA initialization space. Accordingly, we added new experiments and also removed inappropriate experiments (those without catastrophic forgetting)

Reimagining Anomalies: What If Anomalies Were Normal?

重新定义异常：如果异常是正常的呢？

Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Puyu Wang, Alexander Bartscher, Sebastian Josef Vollmer, Sophie Fellenz, Marius Kloft

AI总结 本文提出一种生成多个替代修改以解释异常检测机制的方法，通过语义解释帮助用户探索异常的"如果"场景。

Comments 38 pages, published as a conference paper at AAAI 2026