AAAI

Natural Strategic Ability in Stochastic Multi-Agent Systems

随机多智能体系统中的自然策略能力

Raphaël Berthon, Joost-Pieter Katoen, Munyque Mittelmann, Aniello Murano

AI总结 本文提出在随机多智能体系统中使用自然策略框架，分析了 NatPATL 和 NatPATL* 的模型检查复杂性，展示了在确定性策略限制下 NatPATL 的 NP 完全性和 NatPATL* 的 2NEXPTIME 复杂性。

Comments Extended version of the paper accepted at AAAI 2024

BAMAS: Structuring Budget-Aware Multi-Agent Systems

BAMAS: 构建预算感知的多智能体系统

Liming Yang, Junyu Luo, Xuanzhe Liu, Yiling Lou, Zhenpeng Chen

AI总结 BAMAS通过整数线性规划和强化学习构建预算感知的多智能体系统，有效降低部署成本的同时保持性能。

Comments Accepted by AAAI 2026 (oral paper)

Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation

时光流逝？一种用于时间偏移容忍生理信号转换的元学习框架

Qian Hong, Cheng Bian, Xiao Zhou, Xiaoyu Li, Yelei Li, Zijing Zeng

AI总结 ShiftSyncNet通过元学习框架自动缓解时间偏移对生理信号转换的影响，提升转换精度和标签质量。

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

Prune4Web: DOM Tree Pruning Programming for Web Agent

Prune4Web: 面向Web代理的DOM树剪枝编程

Jiayuan Zhang, Kaiquan Chen, Zhihao Lu, Enshen Zhou, Qian Yu, Jing Zhang

AI总结 Prune4Web通过DOM树剪枝编程提升Web自动化精度，实现25-50倍候选元素减少，显著提高接地任务准确性至88.28%。

Comments Paper accepted to AAAI 2026

The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods

定向预测变化 - 用于局部特征归因方法高效且可信的保真度评估

Kevin Iselborn, David Dembinsky, Adriano Lucieri, Andreas Dengel

AI总结 本文提出DPC度量标准，用于高效且可信地评估局部特征归因方法的保真度，通过改进预测变化度量并消除随机性，实现快速且确定性的评估。

Comments 13 pages, 10 figures, 5 tables, accepted at AAAI SECURE-AI4H workshop

Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling

学习细胞感知的层次多模态表示以实现稳健的分子建模

Mengran Li, Zelin Zang, Wenbin Xing, Junzhou Chen, Ronghui Zhang, Jiebo Luo, Stan Z. Li

AI总结 CHMR通过联合建模分子与细胞反应的层次依赖关系，提升分子建模的稳健性和生物学基础。

Comments Accepted to AAAI 2026 (Oral)

EM-KD: Distilling Efficient Multimodal Large Language Model with Unbalanced Vision Tokens

EM-KD: 通过不平衡视觉标记的知识蒸馏提升高效多模态大语言模型

Ze Feng, Sen Yang, Boqiang Duan, Wankou Yang, Jingdong Wang

机构 * Baidu VIS（百度视觉） 纠错

AI总结 EM-KD通过改进的知识蒸馏方法，有效提升高效多模态大语言模型的视觉理解和效率。

Comments accepted by AAAI 2026

Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLMs

打破安全性与能力的权衡：具有可验证奖励的强化学习在LLMs中维持安全护栏

Dongkyu Derek Cho, Huan Song, Arijit Ghosh Chowdhury, Haotian An, Yawei Wang, Rohit Thekkanal, Negin Sokhandan, Sharlina Keshava, Hannah Marlowe

AI总结 本文提出通过可验证奖励的强化学习方法，在提升LLM推理能力的同时维持安全性，挑战了传统安全性与能力权衡的假设。

Comments AAAI-26 Workshop on Post-AI Formal Methods

LungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules

LungNoduleAgent: 一种用于肺结节精准诊断的协作多智能体系统

Cheng Yang, Hui Jin, Xinlei Yu, Zhipeng Wang, Yaoqun Liu, Fenglei Fan, Dajiang Lei, Gangyong Jia, Changmiao Wang, Ruiquan Ge

AI总结 LungNoduleAgent通过协作多智能体系统提升肺结节诊断的精度与效率

Comments Accepted by AAAI 2026

LOOM: Personalized Learning Informed by Daily LLM Conversations Toward Long-Term Mastery via a Dynamic Learner Memory Graph

LOOM：通过动态学习者记忆图实现每日LLM对话指导的个性化学习以实现长期掌握

Justin Cui, Kevin Pu, Tovi Grossman

AI总结 LOOM通过动态学习者记忆图分析每日LLM对话，生成个性化学习材料以实现长期掌握，兼顾连续性和主动性，提升学习者持续进步与即时相关性的平衡。

Comments Accepted to the PerFM Workshop at AAAI 2026

Knowledge Completes the Vision: A Multimodal Entity-aware Retrieval-Augmented Generation Framework for News Image Captioning

知识完善视觉：一种多模态实体感知检索增强生成框架用于新闻图像描述

Xiaoxing You, Qiang Huang, Lingyu Li, Chi Zhang, Xiaopeng Liu, Min Zhang, Jun Yu

AI总结 MERGE提出了一种多模态实体感知检索增强生成框架，通过构建实体中心知识库和改进跨模态对齐，提升新闻图像描述质量和命名实体识别性能。

Comments Accepted to AAAI 2026

$A^2Flow:$ Automating Agentic Workflow Generation via Self-Adaptive Abstraction Operators

$A^2Flow$：通过自适应抽象运算符自动化代理工作流生成

Mingming Zhao, Xiaokang Wei, Yuanqi Shao, Kaiwen Zhou, Lin Yang, Siwei Rao, Junhui Zhan, Zhitang Chen

AI总结 $A^2Flow$通过自适应抽象运算符实现代理工作流的自动化生成，提升性能并降低资源消耗。

Comments Accepted by AAAI-2026

Democratizing LLM Efficiency: From Hyperscale Optimizations to Universal Deployability

让大语言模型效率普及：从超大规模优化到通用部署

Hen-Hsen Huang

AI总结 本文提出通过改进LLM的效率方法，使其在有限资源和专业知识下仍能有效运作，以实现更广泛的部署和公平性。

Comments 8 pages, accepted as a Blue Sky Talk in AAAI 2026

Connecting the Dots: Training-Free Visual Grounding via Agentic Reasoning

连接点：通过代理推理实现无需训练的视觉接地

Liqin Luo, Guangyao Chen, Xiawu Zheng, Yongxing Dai, Yixiong Zou, Yonghong Tian

AI总结 GroundingAgent通过代理推理实现无需微调的视觉接地，达到65.1%的零样本准确率，并在选择阶段实现90%的准确率，展示了LLM推理能力的重要性。

Comments AAAI 2026

PrefixGPT: Prefix Adder Optimization by a Generative Pre-trained Transformer

PrefixGPT：通过生成式预训练变换器优化前缀加法器

Ruogu Ding, Xin Ning, Ulf Schlichtmann, Weikang Qian

AI总结 PrefixGPT通过生成式预训练变换器优化前缀加法器，实现7.7%的ADP改进和79.1%的平均ADP降低。

Comments This is an extended version of the paper accepted by the AAAI-2026 Conference

Mem-PAL: Towards Memory-based Personalized Dialogue Assistants for Long-term User-Agent Interaction

Mem-PAL: 向基于记忆的个性化对话助手迈进：长期用户-代理交互

Zhaopei Huang, Qifeng Dai, Guozheng Wu, Xiaopeng Wu, Kehan Chen, Chuan Yu, Xubin Li, Tiezheng Ge, Wenxuan Wang, Qin Jin

AI总结 Mem-PAL提出H$^2$Memory框架，通过合成数据和实验验证，提升长期用户-代理交互的个性化能力。

Comments Accepted by AAAI 2026 (Oral)

Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation

小波增强的自适应频率滤波器用于序列推荐

Huayang Xu, Huanhuan Yuan, Guanfeng Liu, Junhua Fang, Lei Zhao, Pengpeng Zhao

AI总结 本文提出小波增强的自适应频率滤波器，通过动态频域滤波和小波特征增强，提升序列推荐的性能和效率。

Comments Accepted by AAAI 2026

TinyChemVL: Advancing Chemical Vision-Language Models via Efficient Visual Token Reduction and Complex Reaction Tasks

TinyChemVL：通过高效视觉标记减少和复杂反应任务推进化学视觉-语言模型

Xuanle Zhao, Shuxin Zeng, Xinyuan Cai, Xiang Cheng, Duzhen Zhang, Xiuyi Chen, Bo Xu

AI总结 TinyChemVL通过高效视觉标记减少和复杂反应任务提升化学视觉-语言模型的效率和推理能力，实现更高效的化学任务处理。

Comments Accepted by AAAI 2026

EvoEmpirBench: Dynamic Spatial Reasoning with Agent-ExpVer

EvoEmpirBench: 基于智能体经验的动态空间推理

Pukun Zhao, Longxiang Wang, Miaowei Wang, Chen Chen, Fanqing Zhou, Haojian Huang

机构 * Guangdong University of Finance and Economics（广东金融学院） ； Chongqing University（重庆大学） ； University of Edinburgh（爱丁堡大学） ； The University of Hong Kong（香港大学） 纠错

AI总结 EvoEmpirBench通过动态空间基准评估模型在部分可观测和动态环境下的空间推理与记忆能力，揭示主流模型的限制并提供未来研究平台。

Comments Accepted by AAAI 2026, 29 pages, 3 figures, 7 tables

Where to Start Alignment? Diffusion Large Language Model May Demand a Distinct Position

对齐何处开始？扩散大语言模型可能需要不同的位置

Zhixin Xie, Xurui Song, Jun Luo

AI总结 本文提出MOSA方法，通过强化学习对齐dLLM中间生成与安全拒绝，提升安全性。

Comments Accepted for oral presentation at AAAI 2026

CAMERA: Multi-Matrix Joint Compression for MoE Models via Micro-Expert Redundancy Analysis

CAMERA：通过微专家冗余分析实现多矩阵联合压缩以优化MoE模型

Yuzhuang Xu, Xu Han, Yuanchi Zhang, Yixuan Wang, Yijun Liu, Shiyu Ji, Qingfu Zhu, Wanxiang Che

AI总结 CAMERA通过微专家冗余分析实现多矩阵联合压缩，提升MoE模型的压缩效率与性能。

Comments Accepted in AAAI 2026

Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking

哈希水印作为过滤器：在基于权重的神经网络水印中对抗伪造和覆盖攻击

Yuan Yao, Jin Song, Jian Jin

AI总结 NeuralMark通过哈希水印过滤器和平均池化技术，提升基于权重的神经网络水印鲁棒性，有效防御伪造和覆盖攻击。

Comments Accepted by AAAI 2026

Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation

Earth-Adapter: 通过频率适应混合填补地理域的空白

Xiaoxing Hu, Ziyang Gong, Yupei Wang, Yuru Jia, Fei Lin, Dexiang Gao, Ke An, Jianhong Han, Zhuoran Sun, Gen Luo, Gen Luo, Xue Yang

AI总结 Earth-Adapter通过频率适应混合方法有效解决遥感图像中的伪影问题，提升基础模型在遥感任务中的性能。

Comments AAAI 2026 camera ready