AAAI

Vision-Only Gaussian Splatting for Collaborative Semantic Occupancy Prediction

仅视觉的高斯散射用于协作语义占用预测

Cheng Chen, Hao Huang, Saurabh Bagchi

AI总结 本研究提出基于稀疏3D语义高斯散射的协作语义占用预测方法，通过高效融合和减少通信开销，提升了协作感知的性能和鲁棒性。

Comments Accepted by AAAI 2026 (Oral)

Whispering Agents: An Event-driven Covert Communication Protocol For the Internet of Agents

低语代理：一种基于事件的隐秘通信协议用于代理互联网

Kaibo Huang, Yukun Wei, Wansheng Wu, Tianhua Zhang, Zhongliang Yang, Linna Zhou

AI总结 本文提出了一种基于事件的隐秘通信协议ΠCCAP，利用代理对话的特性实现安全隐秘通信，为代理互联网的安全监控和防御提供基础支持。

Comments Accepted to AAAI-26 (Main, Oral)

Backdoors in Conditional Diffusion: Threats to Responsible Synthetic Data Pipelines

条件扩散中的后门：对负责任的合成数据管道的威胁

Raz Lapid, Almog Dubin

机构 * Raz Lapid ； Almog Dubin 纠错

AI总结 本文提出了一种针对ControlNet的模型污染攻击，通过后门生成攻击者指定内容，并提出CFT方法作为防御措施。

Comments Accepted at RDS @ AAAI 2026

Journal ref AAAI 2026 Workshop on Shaping Responsible Synthetic Data in the Era of Foundation Models

ReCode: Updating Code API Knowledge with Reinforcement Learning

ReCode: 通过强化学习更新代码API知识

Haoze Wu, Yunzhi Yao, Wenhao Yu, Ningyu Zhang

AI总结 ReCode通过强化学习方法提升LLMs在动态API环境下的代码生成能力，尤其在CodeUpdateArena任务中表现优异。

Comments AAAI 2026

Graph of Verification: Structured Verification of LLM Reasoning with Directed Acyclic Graphs

验证图：基于有向无环图的结构化LLM推理验证

Jiwei Fang, Bin Zhang, Changwei Wang, Jin Wan, Zhiwei Xu

AI总结 本文提出验证图（GoV），通过灵活的节点块架构实现适应性多粒度验证，有效解决LLM推理验证中的精度与鲁棒性平衡问题。

Comments Accepted to AAAI 2026

LoKI: Low-damage Knowledge Implanting of Large Language Models

LoKI: 大型语言模型的低损伤知识植入

Runyu Wang, Peng Ping, Zhengyu Guo, Xiaoye Zhang, Quan Shi, Liting Zhou, Tianbo Ji

AI总结 LoKI通过利用transformer架构的知识存储机制，实现了在保持通用能力的同时提升任务特定性能的参数高效微调方法。

Comments AAAI-26 Oral

A Roadmap to Guide the Integration of LLMs in Hierarchical Planning

引导大型语言模型在分层规划中整合的路线图

Israel Puerta-Merino, Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares

AI总结 本文提出了一条引导大型语言模型在分层规划中整合的路线图，通过分类整合方法和建立基准测试，探索LLMs在分层规划中的应用潜力。

Comments 5 pages, 0 figures, to be published in the AAAI Workshop on Planning in the Era of LLMs ( https://llmforplanning.github.io )

Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN

从全局相关性学习：通过谱图神经网络增强进化算法

Kaichen Ouyang, Zong Ke, Shengwei Fu, Lingjie Liu, Puning Zhao, Dayu Hu

AI总结 GNE通过谱图神经网络增强进化算法，利用频率过滤策略提升全局信息利用，解决探索与利用平衡问题，实现更高效的优化性能。

Comments Accepted by the 40th Annual AAAI Conference on Artificial Intelligence

Inductive Generative Recommendation via Retrieval-based Speculation

基于检索的生成推荐

Yijie Ding, Jiacheng Li, Julian McAuley, Yupeng Hou

AI总结 SpecGR通过引入检索生成机制，提升生成推荐模型在归纳设置下的推荐能力与整体性能。

Comments Accepted to AAAI 2026 (oral)

PriorDrive: Enhancing Online HD Mapping with Unified Vector Priors

PriorDrive: 通过统一向量先验增强在线高精度地图

Shuang Zeng, Xinyuan Chang, Xinran Liu, Yujian Yuan, Shiyi Liang, Zheng Pan, Mu Xu, Xing Wei

机构 * Shuang Zeng 1,2*† , Xinyuan Chang 2† , Xinran Liu 2 , Yujian Yuan 3 , Shiyi Liang 1,2 , Zheng Pan 2 , Mu Xu 2 , Xing Wei 1‡（未知） 纠错

AI总结 PriorDrive通过整合多种向量化先验地图，提升在线高精度地图构建的鲁棒性和准确性。

Comments AAAI 2026; Code: https://github.com/MIV-XJTU/PriorDrive