AAAI

Seeing the Unseen: Zooming in the Dark with Event Cameras

看见不可见的：利用事件相机在黑暗中放大

Dachun Kai, Zeyu Xiao, Huyue Zhu, Jiaxiao Wang, Yueyi Zhang, Xiaoyan Sun

AI总结 RetinexEVSR通过事件驱动和Retinex先验知识提升低光视频超分辨率性能，实现高对比度细节恢复与低光伪影抑制。

Comments Accepted to AAAI 2026

BiPrompt: Bilateral Prompt Optimization for Visual and Textual Debiasing in Vision-Language Models

BiPrompt：面向视觉与文本去偏的双重视觉语言模型双向提示优化

Sunny Gupta, Shounak Das, Amit Sethi

AI总结 BiPrompt通过双重视觉语言模型双向提示优化，同时减轻视觉和文本中的非因果特征依赖，提升模型在分布偏移下的鲁棒性和因果推理能力。

Comments Accepted at the AAAI 2026 Workshop AIR-FM, Assessing and Improving Reliability of Foundation Models in the Real World

XAI-MeD: Explainable Knowledge Guided Neuro-Symbolic Framework for Domain Generalization and Rare Class Detection in Medical Imaging

XAI-MeD: 可解释知识引导的神经符号框架用于医学影像中的领域泛化和稀有类别检测

Midhat Urooj, Ayan Banerjee, Sandeep Gupta

AI总结 XAI-MeD通过整合临床知识的神经符号框架，提升医学影像中领域泛化和稀有类别检测的性能，实现更鲁棒和可解释的多模态医学AI。

Comments Accepted at AAAI Bridge Program 2026

MacVQA: Adaptive Memory Allocation and Global Noise Filtering for Continual Visual Question Answering

MacVQA: 适应性内存分配与全局噪声过滤用于连续视觉问答

Zhifei Li, Yiran Wang, Chenyi Xiong, Yujing Xia, Xiaoju Hou, Yue Zhao, Miao Zhang, Kui Xiao, Bing Yang

AI总结 MacVQA通过适应性内存分配和全局噪声过滤提升持续视觉问答的性能，实现更高的准确率和更低的遗忘率。

Comments Accepted to AAAI 2026

Forget Less by Learning from Parents Through Hierarchical Relationships

通过层级关系从父母学习来减少遗忘

Arjun Ramesh Kaushik, Naresh Kumar Devulapally, Vishnu Suresh Lokhande, Nalini K. Ratha, Venu Govindaraju

AI总结 FLLP通过在超几何空间中引入父-子跨概念学习机制，减少生成模型在连续学习中的遗忘问题，提升模型的鲁棒性和泛化能力。

Comments Accepted at AAAI-26

Vision-Language Reasoning for Geolocalization: A Reinforcement Learning Approach

基于视觉-语言推理的地理定位：一种强化学习方法

Biao Wu, Meng Fang, Ling Chen, Ke Xu, Tao Cheng, Jun Wang

AI总结 本文提出Geo-R，一种基于强化学习的无检索地理定位框架，通过链式区域生成可解释的监督，提升定位准确性与可解释性。

Comments Accepted to AAAI 2026. Project Page: https://github.com/aialt/geo-r

Improved Runtime Guarantees for the SPEA2 Multi-Objective Optimizer

SPEA2多目标优化器的改进运行时间保证

Benjamin Doerr, Martin S. Krejca, Milan Stanković

AI总结 SPEA2在多目标优化中通过改进的运行时间保证，更有效地处理特定基准问题。

Comments Accepted for AAAI 2026

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

TimeMosaic: 通过自适应粒度补丁和分段解码指导的时间序列预测

Kuiye Ding, Fanda Fan, Chunyi Hou, Zheya Wang, Lei Wang, Zhengxin Yang, Jianfeng Zhan

AI总结 TimeMosaic通过自适应粒度补丁和分段解码，解决时间序列预测中的时间异质性问题，提升预测精度与效率。

Comments This paper has been accepted by AAAI

Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack

针对联邦学习中资源受限和数据异质性的双重权重稀疏打包方法

Qiantao Yang, Liquan Chen, Mingfu Xue, Songze Li

AI总结 FedCSPACK通过余弦稀疏打包和双权重聚合方法，有效解决联邦学习中资源受限和数据异质性问题，提升通信和计算效率的同时保持高模型精度。

Comments Accepted in AAAI 2026

Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment

多粒度交互注意力框架用于残差分层发音评估

Hong Han, Hao-Chen Pei, Zhao-Zheng Nie, Xin Luo, Xin-Shun Xu

AI总结 本文提出了一种多粒度交互注意力框架，通过双向建模和残差分层结构提升发音评估性能。

Comments 9 pages, 4 figures, 5 tables, accepted by AAAI 2026

From Theory of Mind to Theory of Environment: Counterfactual Simulation of Latent Environmental Dynamics

从心智理论到环境理论：潜在环境动态的反事实模拟

Ryutaro Uchiyama

机构 * Ryutaro Uchiyama（独立研究者） 纠错

AI总结 本文提出'环境理论'，通过社会线索推断隐藏环境动态，利用与心智理论共享的计算机制，扩展运动探索维度以促进行为创新。

Comments Accepted to the AAAI 2026 Workshop on Theory of Mind for Artificial Intelligence (ToM4AI). Extended abstract, 2 pages

Towards Long-window Anchoring in Vision-Language Model Distillation

迈向视觉-语言模型蒸馏中的长窗口锚定

Haoyi Zhou, Shuo Li, Tianyu Chen, Qi Song, Chonghan Gao, Jianxin Li

AI总结 本文提出LAid方法，通过改进注意力机制和RoPE响应调节，提升视觉-语言模型的长上下文理解能力，使蒸馏模型的有效上下文窗口长度增加3.2倍。

Comments Accepted by AAAI 2026

DISCODE: Distribution-Aware Score Decoder for Robust Automatic Evaluation of Image Captioning

DISCODE：面向分布的评分解码器，用于鲁棒的图像描述评估

Nakamasa Inoue, Kanoko Goto, Masanari Oi, Martyna Gruszka, Mahiro Ukai, Takumi Hirose, Yusuke Sekikawa

AI总结 DISCODE是一种无需微调的鲁棒图像描述评估方法，通过自适应测试时损失和多领域基准提升评估鲁棒性。

Comments Paper accepted to AAAI 2026

InfoDecom: Decomposing Information for Defending Against Privacy Leakage in Split Inference

InfoDecom：分解信息以防止在分割推断中的隐私泄露

Ruijun Deng, Zhihui Lu, Qiang Duan

AI总结 InfoDecom通过分解和去除冗余信息并注入校准噪声，实现了在分割推断中更优的隐私保护与效用平衡。

Comments 11pages, 6figures. Accepted by AAAI 2026

Rethinking Saliency Maps: A Cognitive Human Aligned Taxonomy and Evaluation Framework for Explanations

重新思考显著性图：一种与人类认知对齐的分类和评估框架用于解释

Yehonatan Elisha, Seffi Cohen, Oren Barkan, Noam Koenigstein

AI总结 本文提出RFxG分类法，通过区分点对点与对比性解释及不同粒度，改进显著性图的评估框架，以更符合人类认知需求。

Journal ref AAAI 2026, Paper page: https://yonisgit.github.io/rfxg1/

Optimal Look-back Horizon for Time Series Forecasting in Federated Learning

联邦学习中时间序列预测的最优回溯范围

Dahao Tang, Nan Yang, Yanli Li, Zhiyu Zhu, Zhibo Jin, Dong Yuan

AI总结 本文提出了一种在联邦学习中通过内在空间公式进行时间序列预测回溯范围选择的原理性框架，通过合成数据生成器和损失分解理论，为适应性回溯范围选择提供了理论支持。

Comments Accepted by AAAI-26 as Oral Presentation

Behaviour Policy Optimization: Provably Lower Variance Return Estimates for Off-Policy Reinforcement Learning

行为策略优化：为离线策略强化学习提供可证明的更低方差回报估计

Alexander W. Goodall, Edwin Hamel-De le Court, Francesco Belardinelli

AI总结 本文提出了一种通过行为策略收集数据以降低回报估计方差的方法，提升离线强化学习的样本效率和性能。

Comments Main Track at AAAI 2026

MPJudge: Towards Perceptual Assessment of Music-Induced Paintings

MPJudge：迈向音乐诱导绘画的感知评估

Shiqi Jiang, Tianyi Liang, Huayuan Ye, Changbo Wang, Chenhui Li

AI总结 MPJudge通过调制融合机制和直接偏好优化，提升音乐诱导绘画的感知评估精度。

Journal ref AAAI 2026

Affordance-Guided Coarse-to-Fine Exploration for Base Placement in Open-Vocabulary Mobile Manipulation

基于 affordance 的粗到细探索用于开放词汇移动操控中的基座放置

Tzu-Jung Lin, Jia-Fong Yeh, Hung-Ting Su, Chung-Yi Lin, Yi-Ting Chen, Winston H. Hsu

AI总结 本文提出了一种基于 affordance 的粗到细探索方法，通过结合视觉语言模型的语义理解和几何可行性，提升开放词汇移动操控中基座放置的成功率。

Comments Accepted to AAAI 2026

G2L:From Giga-Scale to Cancer-Specific Large-Scale Pathology Foundation Models via Knowledge Distillation

G2L：从千兆级到癌症特异性大规模病理基础模型的知识蒸馏

Yesung Cho, Sungmin Lee, Geongyu Lee, Minkyung Lee, Jongbae Park, Dongmyung Shin

AI总结 G2L框架通过知识蒸馏将千兆级模型能力转移到大规模模型，以更少的参数和数据实现癌症特异性任务的高性能表现。

Comments Accepted in AAAI 2026 workshop in Health Intelligence Special Theme on Foundation Models and AI Agents

Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model

使用标记条件扩散模型进行虚拟多标记染色

Hyun-Jic Oh, Junsik Kim, Zhiyi Shi, Yichen Wu, Yu-An Chen, Peter K Sorger, Hanspeter Pfister, Won-Ki Jeong

AI总结 本文提出了一种基于条件扩散模型的虚拟多标记染色方法，利用预训练模型生成多标记图像，提升了H&E图像的分子层面分析能力。

Comments Accepted at AAAI 2026

SpeakerLM: End-to-End Versatile Speaker Diarization and Recognition with Multimodal Large Language Models

SpeakerLM：基于多模态大语言模型的端到端多功能说话人辨识与识别

Han Yin, Yafeng Chen, Chong Deng, Luyao Cheng, Hui Wang, Chao-Hong Tan, Qian Chen, Wen Wang, Xiangang Li

AI总结 SpeakerLM通过多模态大语言模型实现端到端的说话人辨识与识别，结合灵活的说话人注册机制，提升多说话人场景下的识别性能。

Comments Accepted by AAAI 2026

SDEval: Safety Dynamic Evaluation for Multimodal Large Language Models

SDEval: 多模态大语言模型的安全动态评估

Hanqing Wang, Yuan Tian, Mingyu Liu, Zhenhao Zhang, Xiangyang Zhu

AI总结 SDEval通过动态调整安全基准分布和复杂度，提升多模态大语言模型的安全评估效果，缓解数据污染并揭示模型安全局限。

Comments AAAI 2026 poster

PriorRG: Prior-Guided Contrastive Pre-training and Coarse-to-Fine Decoding for Chest X-ray Report Generation

PriorRG: 基于先验知识的对比预训练与粗到细解码用于胸部X光报告生成

Kang Liu, Zhuoqi Ma, Zikang Fang, Yunan Li, Kun Xie, Qiguang Miao

AI总结 PriorRG通过结合先验知识的对比预训练和粗到细解码，提升胸部X光报告生成的准确性和流畅性。

Comments Accepted by AAAI 2026

Modeling Item-Level Dynamic Variability with Residual Diffusion for Bundle Recommendation

基于残差扩散的捆绑推荐中项目级动态变异建模

Dong Zhang, Lin Li, Ming Li, Amran Bhuiyan, Meng Sun, Xiaohui Tao, Jimmy Xiangji Huang

AI总结 本文提出RDiffBR模型，通过残差扩散方法提升捆绑推荐中项目级动态变异适应能力，使Recall和NDCG指标提升达23%。

Comments Extended version for AAAI'26

Language Model Distillation: A Temporal Difference Imitation Learning Perspective

语言模型蒸馏：一种时差模仿学习视角

Zishun Yu, Shangzhe Li, Xinhua Zhang

AI总结 本文提出一种基于时差学习的通用蒸馏框架，利用教师模型的分布稀疏性，通过缩减动作空间提升语言模型蒸馏效果。

Comments AAAI 2026; Code available at: https://github.com/TobyLeelsz/Bellman-Distillation

SinBasis Networks: Matrix-Equivalent Feature Extraction for Wave-Like Optical Spectrograms

SinBasis网络：用于波形光学光谱图的矩阵等价特征提取

Yuzhou Zhu, Zheng Zhang, Ruyi Zhang, Liang Zhou

AI总结 SinBasis网络通过引入正弦映射的矩阵等价框架，提升波形图像的特征提取能力，实现对周期性模式的高敏感性和空间位移不变性。

Comments AAAI26 Poster

Journal ref The 40th Annual AAAI Conference on Artificial Intelligence (AAAI2026)

TraveLLaMA: A Multimodal Travel Assistant with Large-Scale Dataset and Structured Reasoning

TraveLLaMA: 一种基于大规模数据集和结构化推理的多模态旅行助手

Meng Chu, Yukang Chen, Haokun Gui, Shaozuo Yu, Yi Wang, Jiaya Jia

AI总结 TraveLLaMA通过结构化推理框架和大规模数据集，提升旅行推荐和场景理解能力，实现用户满意度和系统性能的显著提升。

Comments AAAI 2026 Oral

Sentient: Detecting APTs Via Capturing Indirect Dependencies and Behavioral Logic

Sentient：通过捕捉间接依赖和行为逻辑检测APT

Wenhao Yan, Ning An, Wei Qiao, Weiheng Wu, Bo Jiang, Zhigang Lu, Baoxu Liu, Junrong Liu

AI总结 Sentient通过结合预训练和意图分析，利用图Transformer和意图分析模块有效检测APT，显著降低误报率。

Comments Accepted at AAAI 2026

MotionCharacter: Fine-Grained Motion Controllable Human Video Generation

MotionCharacter: 高精度可调控的人体视频生成

Haopeng Fang, Di Qiu, Binjie Mao, He Tang

机构 * Meituan（美团） 纠错

AI总结 MotionCharacter通过解耦动作类型与运动强度，实现高保真人体视频生成，提升细粒度运动控制与身份一致性。

Comments Accepted by AAAI 2026