AAAI

FineTec: Fine-Grained Action Recognition Under Temporal Corruption via Skeleton Decomposition and Sequence Completion

FineTec: 通过骨骼分解和序列补全实现受时间腐蚀影响的细粒度动作识别

Dian Shao, Mingfei Shi, Like Liu

AI总结 FineTec通过骨骼分解和序列补全方法，在受时间腐蚀影响下实现细粒度动作识别的高效准确识别。

Comments Accepted by AAAI 2026

MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes

MAMA-Memeia! 多维度多智能体协作在表情包抑郁症状识别中的应用

Siddhant Agarwal, Adya Dhuler, Polly Ruhnke, Melvin Speisman, Md Shad Akhtar, Shweta Yadav

AI总结 MAMAMemeia通过多智能体多维度协作框架，利用CAT能力提升表情包抑郁症状识别的宏F1指标，达到7.55%的提升，成为新的基准模型。

Comments Accepted by AAAI 2026

UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning

UniC-Lift: 通过对比学习实现统一的3D实例分割

Ankit Dhiman, Srinath R, Jaswanth Reddy, Lokesh R Boregowda, Venkatesh Babu Radhakrishnan

AI总结 UniC-Lift通过对比学习实现统一的3D实例分割，引入可学习特征嵌入和改进的嵌入到标签过程，提升分割性能并减少训练时间。

Comments Accepted to AAAI 2026. Project Page: https://unic-lift.github.io/

Renormalization Group Guided Tensor Network Structure Search

基于重整化群的张量网络结构搜索

Maolin Wang, Bowen Yu, Sheng Zhang, Linjie Mi, Wanyu Wang, Yiqi Wang, Pengyue Jia, Xuetao Wei, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao

AI总结 RGTN通过多尺度重整化群流引导张量网络结构搜索，实现高效优化和结构演化，取得优异的压缩比和计算效率。

Comments Accepted to AAAI 2026

RAGPart & RAGMask: Retrieval-Stage Defenses Against Corpus Poisoning in Retrieval-Augmented Generation

RAGPart & RAGMask：对抗检索增强生成中语料污染的检索阶段防御

Pankayaraj Pathmanathan, Michael-Andrei Panaitescu-Liess, Cho-Yu Jason Chiang, Furong Huang

AI总结 本文提出RAGPart和RAGMask两种检索阶段防御方法，用于对抗RAG中语料污染攻击，通过文档分区和标记遮蔽技术降低攻击成功率，提升RAG系统的鲁棒性。

Comments Published at AAAI 2026 Workshop on New Frontiers in Information Retrieval [Oral]

MambaSeg: Harnessing Mamba for Accurate and Efficient Image-Event Semantic Segmentation

MambaSeg: 利用Mamba实现准确且高效的图像事件语义分割

Fuqiang Gu, Yuanke Li, Xianlei Long, Kangping Ji, Chao Chen, Qingyi Gu, Zhenliang Ni

AI总结 MambaSeg通过双分支框架和双维交互模块，实现高效准确的多模态语义分割，适用于快速运动和低光条件下的图像事件分割任务。

Comments Accepted by AAAI 2026

Graph-Based Exploration for ARC-AGI-3 Interactive Reasoning Tasks

基于图的探索用于ARC-AGI-3交互推理任务

Evgenii Rudakov, Jonathan Shock, Benjamin Ultan Cowley

AI总结 基于图的探索方法在ARC-AGI-3任务中实现高效交互推理，无需训练即可解决大量级别，优于LLM方法。

Journal ref AAAI 2026 Workshop on AI for Scientific Research

Neighbor-aware Instance Refining with Noisy Labels for Cross-Modal Retrieval

具有噪声标签的邻居感知实例细化用于跨模态检索

Yizhi Liu, Ruitao Pu, Shilin Xu, Yingke Chen, Quan-Hui Liu, Yuan Sun

AI总结 本文提出了一种新的跨模态学习框架NIRNL，通过引入噪声标签和邻居感知机制，提升模型在高噪声环境下的检索性能。

Comments 9 pages, 4 figures, and AAAI-26 conference

Beyond Hallucinations: A Composite Score for Measuring Reliability in Open-Source Large Language Models

超越幻觉：一种衡量开源大语言模型可靠性的综合评分

Rohit Kumar Salla, Manoj Saravanan, Shrikar Reddy Kota

AI总结 本文提出综合可靠性评分（CRS）用于评估开源大语言模型的可靠性，通过实验发现最可靠的系统在准确率、鲁棒性和校准不确定性间取得平衡。

Comments 5 pages, 4 tables, accepted at AAAI 2026

CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards

CEC-Zero: 无监督字符错误纠正与自生成奖励

Zhiming Lin, Kai Zhao, Sophie Zhang, Peilai Yu, Canran Xiao

AI总结 CEC-Zero通过无监督强化学习框架，利用自动生成的奖励在无需标注的情况下提升大语言模型的字符错误纠正能力，实现了在多个基准测试中的性能提升。

Comments AAAI'26 poster

GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting

GaussianImage++: 通过2D高斯散射提升图像表示与压缩

Tiantian Li, Xinjie Zhang, Xingtong Ge, Tongda Xu, Dailan He, Jun Zhang, Yan Wang

AI总结 GaussianImage++通过有限的高斯原始体实现高效的图像表示与压缩，结合驱动扭曲的密集化机制和上下文感知滤波器，提升性能并降低内存消耗。

Comments Accepted to AAAI 2026. Code URL:https://github.com/Sweethyh/GaussianImage_plus.git

FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI

FDP：一种用于脑部MRI无监督异常检测的频率分解预处理流程

Hao Li, Zhenfeng Zhuang, Jingyu Lin, Yu Liu, Yifei Chen, Qiong Peng, Lequan Yu, Liansheng Wang

AI总结 FDP通过频域分解提升脑部MRI无监督异常检测性能，实现病灶抑制与解剖保持的平衡。

Comments Accepted by AAAI 2026

Open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison

Open-sci-ref-0.01: 开放和可重复的参考基线用于语言模型和数据集比较

Marianna Nezhurina, Jörg Franke, Taishi Nakamura, Timur Carstensen, Niccolò Ajroldi, Ville Komulainen, David Salinas, Jenia Jitsev

AI总结 open-sci-ref通过开放和可重复的参考基线，为语言模型和数据集的比较提供标准化评估框架，支持训练过程的比较与未来研究。

Comments v.1.1. AAAI Workshop on Reproducible Artificial Intelligence (RAI, https://reproducibleai.github.io) 2026, camera ready version. Model weights and intermediate training checkpoints are available at https://huggingface.co/collections/open-sci/open-sci-ref-001; code for reproducing training, evaluation and raw experiments data at https://github.com/LAION-AI/open-sci-ref-0.01

RAST: A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction

RAST：一种用于交通预测的检索增强时空框架

Weilin Ruan, Xilin Dang, Ziyu Zhou, Sisuo Lyu, Yuxuan Liang

AI总结 RAST通过整合检索增强机制与时空建模，解决交通预测中的上下文容量和细粒度预测性挑战，实现高效且准确的交通预测。

Comments Accepted by AAAI 2026 (AI for Social Impact)

Generalising Traffic Forecasting to Regions without Traffic Observations

面向无交通观测区域的交通预测泛化

Xinyu Su, Majid Sarvi, Feng Liu, Egemen Tanin, Jianzhong Qi

机构 * The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）） 纠错

AI总结 本文提出GenCast模型，通过整合物理信息神经网络和外部信号学习模块，提升无交通观测区域的交通预测泛化能力。

Comments Accepted by AAAI 2026

SplatSSC: Decoupled Depth-Guided Gaussian Splatting for Semantic Scene Completion

SplatSSC：解耦深度引导的高斯点云法用于语义场景补全

Rui Qian, Haozhi Cao, Tianchen Deng, Shenghai Yuan, Lihua Xie

AI总结 SplatSSC通过深度引导的初始化策略和解耦高斯聚合器，提升语义场景补全的效率与鲁棒性，实现性能和资源消耗的优化。

Comments Accepted for oral presentation in The 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026)

Learning Network Dismantling Without Handcrafted Inputs

无需人工输入的学习网络拆解

Haozhe Tian, Pietro Ferraro, Robert Shorten, Mahdi Jalili, Homayoun Hamedmoghadam

AI总结 MIND通过消息迭代机制和合成网络训练，无需人工输入高效解决网络拆解问题，适用于大规模真实网络。

Comments Accepted for Oral Presentation at the 40th AAAI Conference on Artificial Intelligence (AAAI-26), Main Technical Track

An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation

检索增强生成方法的超参数优化分析

Matan Orbach, Ohad Eytan, Benjamin Sznajder, Ariel Gera, Odellia Boni, Yoav Kantor, Gal Bloch, Omri Levy, Hadas Abraham, Nitzan Barzilay, Eyal Shnarch, Michael E. Factor, Shila Ofek-Koifman, Paula Ta-Shma, Assaf Toledo

AI总结 本文分析了RAG超参数优化方法，通过五种算法和五种数据集的实验，发现贪婪或随机搜索能高效提升RAG性能，且优先优化模型选择优于传统流程顺序。

Comments AAAI 2026 Workshop on New Frontiers in Information Retrieval. For associated results, see https://github.com/IBM/rag-hpo-bench

HINTS: Extraction of Human Insights from Time-Series Without External Sources

HINTS: 从时间序列中提取人类洞察力而不依赖外部数据源

Sheo Yon Jhin, Noseong Park

机构 * Sheo Yon Jhin, Noseong Park 纠错

AI总结 HINTS通过自监督学习从时间序列残差中提取人类洞察力，无需外部数据源，提升预测准确性并增强可解释性。

Comments AAAI 2026 AI4TS Workshop paper

BCWildfire: A Long-term Multi-factor Dataset and Deep Learning Benchmark for Boreal Wildfire Risk Prediction

BCWildfire: 一种长期多因素数据集和深度学习基准，用于北极火灾风险预测

Zhengsen Xu, Sibo Cheng, Lanying Wang, Hongjie He, Wentao Sun, Jonathan Li, Lincoln Linlin Xu

AI总结 BCWildfire数据集和基准为长期多因素火灾风险预测提供了深度学习评估框架，包含38个协变量和多种模型评估。

Comments This paper has been accepted by AAAI-26

ParetoHqD: Fast Offline Multiobjective Alignment of Large Language Models using Pareto High-quality Data

ParetoHqD: 利用帕累托高质量数据快速实现大语言模型的多目标对齐

Haoran Gu, Handing Wang, Yi Mei, Mengjie Zhang, Yaochu Jin

AI总结 ParetoHqD通过利用帕累托高质量数据实现大语言模型的多目标对齐，提升对齐效果和效率。

Comments Accepted as a main conference paper at AAAI 2026