AAAI

RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labels

RSOD：基于极有限标签的可靠性引导声纳图像目标检测

Chengzhou Li, Ping Guo, Guanchen Meng, Qi Jia, Jinyuan Liu, Zhu Liu, Xiaokang Liu, Yu Liu, Zhongxuan Luo, Xin Fan

AI总结 RSOD通过可靠性引导的教师-学生框架，在极有限标签下实现声纳图像目标检测，利用伪标签策略提升性能，实验表明其在UATD数据集上表现优异。

Comments Accepted by AAAI 2026,9 pages,10 figures

VILTA: A VLM-in-the-Loop Adversary for Enhancing Driving Policy Robustness

VILTA：一种用于增强驾驶策略鲁棒性的视觉语言模型闭环对抗者

Qimao Chen, Fang Li, Shaoqing Xu, Zhiyi Lai, Zixun Xie, Yuechen Luo, Shengyin Jiang, Hanbing Li, Long Chen, Bing Wang, Yi Zhang, Zhi-Xin Yang

AI总结 VILTA通过整合视觉语言模型到闭环训练中，提升自动驾驶策略在长尾事件中的安全性和鲁棒性。

Comments Accepted to AAAI 2026

Life, Machine Learning, and the Search for Habitability: Predicting Biosignature Fluxes for the Habitable Worlds Observatory

生命、机器学习与宜居性探索：为宜居世界观测站预测生物特征信号流量

Mark Moussa, Amber V. Young, Brianna Isola, Vasuda Trehan, Michael D. Himes, Nicholas Wogan, Giada Arney

AI总结 本文提出两种机器学习模型，用于预测系外行星反射光光谱中的生物特征信号流量，以提高宜居世界观测站等任务的观测效率和科学回报。

Comments 8 pages, 4 figures. Submitted and accepted in AAAI-26 (IAAI Emerging Applications track)

Class-Partitioned VQ-VAE and Latent Flow Matching for Point Cloud Scene Generation

类分区的VQ-VAE与潜在流匹配用于点云场景生成

Dasith de Silva Edirimuni, Ajmal Saeed Mian

AI总结 本文提出类分区VQ-VAE与潜在流匹配模型，实现无需外部数据库的点云场景生成，有效减少重建误差。

Comments Accepted to AAAI 2026, Main Technical Track

NADIR: Differential Attention Flow for Non-Autoregressive Transliteration in Indic Languages

NADIR：非自回归翻译中的差分注意力流

Lakshya Tomar, Vinayak Abrol, Puneet Agarwal

AI总结 NADIR通过差分Transformer和专家混合机制，在多语言转写任务中实现高速且高准确性的非自回归翻译系统。

Comments Accepted at the AAAI Conference on Artificial Intelligence (AAAI 2026)

Concepts from Representations: Post-hoc Concept Bottleneck Models via Sparse Decomposition of Visual Representations

表示法中的概念：通过视觉表示的稀疏分解实现事后概念瓶颈模型

Shizhan Gong, Xiaofan Zhang, Qi Dou

AI总结 本文提出PCBM-ReD，通过视觉表示的稀疏分解，实现对预训练模型的可解释性增强，提升图像分类任务的准确性和可解释性。

Comments AAAI 2026

Towards Temporal Fusion Beyond the Field of View for Camera-based Semantic Scene Completion

超越视野范围的基于摄像头的语义场景补全

Jongseong Bae, Junwoo Ha, Jinnyeong Heo, Yeongin Lee, Ha Young Kim

AI总结 本文提出C3DFusion模块，通过融合历史和当前帧的3D特征，提升基于摄像头的语义场景补全效果，显著优于现有方法。

Comments Accepted to AAAI 2026

DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs

DeepProofLog: 在深度随机逻辑程序中实现高效的证明

Ying Jiao, Rodrigo Castellano Ontiveros, Luc De Raedt, Marco Gori, Francesco Giannini, Michelangelo Diligenti, Giuseppe Marra

AI总结 DeepProofLog通过引入深度随机逻辑程序和马尔可夫决策过程的映射，提升了神经符号AI在复杂证明空间和大规模知识库中的可扩展性。

Comments Accepted as an Oral at AAAI 2026

SpikCommander: A High-performance Spiking Transformer with Multi-view Learning for Efficient Speech Command Recognition

SpikCommander: 一种高性能的脉冲变压器与多视图学习相结合的高效语音命令识别方法

Jiaqi Wang, Liutao Yu, Xiongri Shen, Sihang Guo, Chenlin Zhou, Leilei Zhao, Yi Zhong, Zhiguo Zhang, Zhengyu Ma

AI总结 SpikCommander通过多视图学习和脉冲时间感知自注意力模块，实现了高效语音命令识别，优于现有SNN方法。

Comments Accepted by The Fortieth AAAI Conference on Artificial Intelligence (AAAI 2026)

PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork

PADiff: 预测性与自适应扩散策略用于即兴团队合作

Hohei Chan, Xinzhi Zhang, Antao Xiang, Weinan Zhang, Mengchen Zhao

AI总结 PADiff通过整合队友预测信息，提升在非平稳即兴团队合作场景中的预测与适应能力，实现多模态协作模式的多样化。

Comments Accepted by AAAI 2026

ProtSAE: Disentangling and Interpreting Protein Language Models via Semantically-Guided Sparse Autoencoders

ProtSAE：通过语义引导的稀疏自编码器解构和解释蛋白质语言模型

Xiangyu Liu, Haodi Lei, Yi Liu, Yang Liu, Wei Hu

AI总结 ProtSAE通过语义引导的稀疏自编码器解构蛋白质语言模型，提升其潜在空间中生物相关特征的可解释性与重建保真度。

Comments Accepted in the 39th AAAI Conference on Artificial Intelligence (AAAI 2026)

Answering the Unanswerable Is to Err Knowingly: Analyzing and Mitigating Abstention Failures in Large Reasoning Models

回答不可回答的问题是明知其错：分析和缓解大推理模型中的回避失败

Yi Liu, Xiangyu Liu, Zequn Sun, Wei Hu

AI总结 本文针对大推理模型在面对不可回答问题时的回避失败问题，提出一种轻量级两阶段方法，通过认知监控与推理干预提升回避率并保持推理性能。

Comments Accepted in the 39th AAAI Conference on Artificial Intelligence (AAAI 2026)

STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes

STRIDE-QA：用于城市驾驶场景时空推理的视觉问答数据集

Keishi Ishihara, Kento Sasaki, Tsubasa Takahashi, Daiki Shiono, Yu Yamaguchi

AI总结 STRIDE-QA通过大规模视觉问答数据集提升自动驾驶中动态交通场景的时空推理能力，显著提升VLMs在空间定位和未来运动预测中的表现。

Comments Accepted to AAAI 2026 (Oral). project page: https://turingmotors.github.io/stride-qa/

DogFit: Domain-guided Fine-tuning for Efficient Transfer Learning of Diffusion Models

DogFit: 域引导微调用于扩散模型高效迁移学习

Yara Bahram, Mohammadhadi Shateri, Eric Granger

AI总结 DogFit通过域感知引导微调提升扩散模型在小目标领域迁移学习的效率与效果，减少计算开销并提高生成质量。

Comments Accepted for poster presentation at AAAI 2026

Hypothesis Generation via LLM-Automated Language Bias for ILP

通过LLM自动语言偏见进行假设生成

Yang Yang, Jiemin Wu, Yutao Yue

AI总结 本文提出通过LLM自动设计语言偏见，结合ILP求解器生成可解释的逻辑规则，提升假设生成的性能和鲁棒性。

Comments accepted by AAAI 2026 Bridge LMReasoning

ParaMETA: Towards Learning Disentangled Paralinguistic Speaking Styles Representations from Speech

ParaMETA: 向学习解耦的语音语义说话风格表示迈进

Haowei Lou, Hye-young Paik, Wen Hu, Lina Yao

AI总结 ParaMETA通过统一框架学习解耦的语音语义说话风格表示，实现多任务处理和生成任务中的细粒度风格控制。

Comments 9 pages, 7 figures, Accepted to AAAI-26 (Main Technical Track)

DeepRAHT: Learning Predictive RAHT for Point Cloud Attribute Compression

DeepRAHT: 基于稀疏张量的端到端预测RAHT点云属性压缩学习

Chunyang Fu, Tai Qin, Shiqi Wang, Zhu Li

AI总结 DeepRAHT通过稀疏张量实现端到端预测RAHT点云属性压缩，提升压缩效率与鲁棒性。

Comments Accepted by AAAI 2026

Speculative Sampling with Reinforcement Learning

推测采样与强化学习

Chenan Wang, Daniel H. Shi, Haipeng Chen

AI总结 Re-SpS通过强化学习优化草稿树超参数，提升大规模语言模型的生成速度，实现高达5.45倍的速度提升。

Comments Accepted to AAAI 2026

Segment and Matte Anything in a Unified Model

统一模型中的分割与遮罩任何事物

Zezhong Fan, Xiaohan Li, Topojoy Biswas, Kaushiki Nag, Kannan Achan

AI总结 本文提出SAMA，一种基于SAM的轻量级模型，实现高质量的交互式图像分割与遮罩，通过多视图定位编码器和定位适配器提升精度，展现广泛的应用能力。

Comments AAAI 2026

Don't Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs

不要重头再来：一种成本有效的LLM之间迁移个性化提示的框架

Ziyi Zhao, Chongming Gao, Yang Zhang, Haoyan Liu, Weinan Gan, Huifeng Guo, Yong Liu, Fuli Feng

AI总结 本文提出PUMA框架，通过参数高效适配器和组基用户选择策略，实现LLM间个性化提示的低成本迁移，实验表明其在计算成本上显著优于全量重训练。

Comments Accepted to AAAI 2026 (Oral). 9 pages, 5 figures

Hybrid IDS Using Signature-Based and Anomaly-Based Detection

基于签名检测和异常检测的混合入侵检测系统

Messaouda Boutassetta, Amina Makhlouf, Newfel Messaoudi, Abdelmadjid Benmachiche, Ines Boutabia

AI总结 本文提出一种结合签名检测和异常检测的混合IDS，旨在提升对新兴网络攻击的检测能力，并探讨其在金融、交通和社交网络中的应用。

Comments 7 pages,The Second National Conference on Artificial Intelligence and Information Technologies (NCAIIT25)

AEMA: Verifiable Evaluation Framework for Trustworthy and Controlled Agentic LLM Systems

AEMA：可验证评估框架用于可信和受控的代理LLM系统

YenTing Lee, Keerthi Koneru, Zahra Moslemi, Sheethal Kumar, Ramesh Radhakrishnan

机构 * University of California, San Diego（加州大学圣地亚哥分校） ； Center for Advanced AI, Accenture（Accenture高级人工智能中心） ； University of California, Irvine（加州大学伊拉斯姆斯分校） 纠错

AI总结 AEMA提出了一种可验证的评估框架，用于评估基于LLM的多代理系统，通过人类监督实现稳定、可追溯的自动化评估。

Comments Workshop on W51: How Can We Trust and Control Agentic AI? Toward Alignment, Robustness, and Verifiability in Autonomous LLM Agents at AAAI 2026

MyGram: Modality-aware Graph Transformer with Global Distribution for Multi-modal Entity Alignment

MyGram: 多模态实体对齐的模态感知图变换器与全局分布

Zhifei Li, Ziyue Qin, Xiangyu Luo, Xiaoju Hou, Yue Zhao, Miao Zhang, Zhifang Huang, Kui Xiao, Bing Yang

AI总结 MyGram通过模态感知图变换器与全局分布机制，提升多模态实体对齐的性能。

Comments Accepted by AAAI 2026

GloCTM: Cross-Lingual Topic Modeling via a Global Context Space

GloCTM：通过全局上下文空间进行跨语言主题建模

Nguyen Tien Phat, Ngo Vu Minh, Linh Van Ngo, Nguyen Thi Ngoc Diep, Thien Huu Nguyen

AI总结 GloCTM通过构建统一语义空间，实现跨语言主题建模的连贯性和语义对齐，提升多语言理解能力。

Comments AAAI 2026

CTPD: Cross Tokenizer Preference Distillation

CTPD: 跨分词器偏好蒸馏

Truong Nguyen, Phi Van Dat, Ngan Nguyen, Linh Ngo Van, Trung Le, Thanh Hong Nguyen

AI总结 CTPD提出一种跨分词器的偏好蒸馏框架，通过对齐跨度投影、跨分词器重要性采样和教师锚定参考，实现不同分词器模型间的偏好信息高效转移。

Comments AAAI 2026

Shapelets-Enriched Selective Forecasting using Time Series Foundation Models

基于时间序列基础模型的形状let增强选择性预测

Shivani Tomar, Seshu Tirupathi, Elizabeth Daly, Ivana Dusparic

AI总结 本文提出基于形状let的选性预测框架，利用时间序列基础模型减少预测误差，提升模型可靠性。

Comments Accepted by the AAAI-26 Workshop on Artificial Intelligence for Time Series Analysis (AI4TS)

POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation

POLARIS：面向后台自动化代理AI的类型化规划与受控执行

Zahra Moslemi, Keerthi Koneru, Yen-Ting Lee, Sheethal Kumar, Ramesh Radhakrishnan

机构 * University of California, Irvine（加州大学尔湾分校） ； Center for Advanced AI, Accenture（Accenture高级人工智能中心） ； University of California, San Diego（加州大学圣地亚哥分校） 纠错

AI总结 POLARIS通过类型化规划和受控执行框架，提升后台自动化中代理AI的政策一致性与可预测性，实现高精度任务处理与审计追踪。

Comments Workshop on Agentic AI Benchmarks and Applications for Enterprise Tasks: AAAI 2026

Measuring Stability Beyond Accuracy in Small Open-Source Medical Large Language Models for Pediatric Endocrinology

在儿科内分泌学中超越准确性的医疗小型开源大语言模型稳定性测量

Vanessa D'Amario, Randy Daniel, Alessandro Zanetti, Dhruv Edamadaka, Nitya Alaparthy, Joshua Tarkoff

AI总结 本文研究了儿科内分泌学中医疗小型开源大语言模型的稳定性，发现高一致性不等于正确性，且系统扰动会影响输出，强调了评估框架的必要性。

Comments 20 pages, 11 figures, accepted at 47 workshop Reproducible Artificial Intelligence (AAAI 2026, Singapore, January 27, 2026)

Multimodal Data Fusion to Capture Dynamic Interactions between Built Environment and Vulnerable Older Adults

多模态数据融合以捕捉建成环境与易受伤害的老年人之间的动态交互

Houhao Liang, Azrin Jamaluddin, Kresimir Friganovic, Kirstie Neo, Raphael Han, Navrag Singh, Panos Mavros

AI总结 本研究通过多模态数据融合技术，探索建成环境对易受伤害老年人移动性的影响，为包容性城市规划提供数据支持。

Comments This work has been accepted to the AAAI 2026 Workshop on AI for Urban Planning

Chatsparent: An Interactive System for Detecting and Mitigating Cognitive Fatigue in LLMs

Chatsparent: 一个用于检测和缓解大语言模型认知疲劳的交互系统

Riju Marwah, Vishal Pallagani, Ritvik Garimella, Amit Sheth

AI总结 Chatsparent通过实时监测和缓解大语言模型的认知疲劳，提升交互体验和模型可靠性。

Comments Accepted to AAAI 2026 Demonstration Track