AAAI

Data Verification is the Future of Quantum Computing Copilots

数据验证是量子计算copilot的未来

Junhao Song, Ziqian Bi, Xinliang Chia, William Knottenbelt, Yudong Cao

AI总结 本研究提出数据验证在量子计算copilot和AI自动化中的重要性，强调验证作为架构基础的必要性，通过实验表明未验证的LLMs在电路优化中准确率仅为79%。

Comments 13 Pages, 20 Figures. Accepted to AAAI 2026 Workshop on AI4Research. Comments welcome

Incremental Maintenance of DatalogMTL Materialisations

增量维护DatalogMTL材料化

Kaiyue Zhao, Dingqi Chen, Shaoyu Wang, Pan Hu

AI总结 本文提出DRedMTL算法，用于高效维护DatalogMTL的增量材料化，通过周期性区间处理提升动态更新效率。

Comments Accepted as oral paper at the main track of AAAI 2026

Bootstrapping Cognitive Agents with a Large Language Model

通过大语言模型提升认知代理

Feiyu Zhu, Reid Simmons

AI总结 通过大语言模型提升认知代理，结合两者优势以提高效率和领域适应性。

Journal ref Proceedings of the AAAI Conference on Artificial Intelligence, 38(1), 655-663 (2024)

Where Norms and References Collide: Evaluating LLMs on Normative Reasoning

规范与参照的碰撞：评估大语言模型在规范推理中的能力

Mitchell Abrams, Kaveh Eskandari Miandoab, Felix Gervits, Vasanth Sarathy, Matthias Scheutz

AI总结 本文提出SNIC测试平台，评估大语言模型在基于规范的参照解析任务中的能力，发现现有模型在处理隐含或冲突规范时存在显著不足。

Comments Accepted to the 40th AAAI Conference on Artificial Intelligence (AAAI-26)

Agentic Observability: Automated Alert Triage for Adobe E-Commerce

代理可观性：Adobe电子商务的自动化警报分拣

Aprameya Bharadwaj, Kyle Tu

AI总结 本文提出了一种基于代理AI的自动化警报分拣框架，通过ReAct范式实现Adobe电子商务系统中警报分拣的自动化，显著提升分拣效率与诊断准确性。

Comments Accepted at AAAI'26 Agentic AI Benchmarks and Applications for Enterprise Tasks Workshop

Deadline-Aware, Energy-Efficient Control of Domestic Immersion Hot Water Heater

具有截止时间感知的家用浸没式热水器节能控制

Muhammad Ibrahim Khan, Bivin Pradeep, James Brusey

AI总结 本文提出了一种基于PPO的截止时间感知控制方法，通过减少能耗在热水器控制中实现了显著的节能效果。

Comments Accepted at AAAI 2026

Rectification Reimagined: A Unified Mamba Model for Image Correction and Rectangling with Prompts

重新定义：一种统一的Mamba模型用于图像校正与矩形化

Linwei Qiu, Gongzhe Li, Xiaozhe Zhang, Qilin Sun, Fengying Xie

AI总结 本文提出统一的Mamba模型UniRect，通过双模块结构解决图像校正与矩形化问题，提升多任务学习中的泛化能力。

Comments AAAI 2026

Align to Structure: Aligning Large Language Models with Structural Information

对齐结构：将大型语言模型与结构信息对齐

Zae Myung Kim, Anand Ramachandran, Farideh Tavazoee, Joo-Kyung Kim, Oleg Rokhlenko, Dongyeop Kang

AI总结 通过整合语言学话语框架到强化学习中，结构对齐方法提升LLMs在长文本生成和摘要任务中的连贯性和结构组织能力。

Comments Accepted to AAAI 2026 AIA

Your AI-Generated Image Detector Can Secretly Achieve SOTA Accuracy, If Calibrated

你的AI生成图像检测器可以秘密实现SOTA精度，如果校准

Muli Yang, Gabriel James Goenawan, Henan Wang, Huaiyuan Qin, Chenghao Xu, Yanhua Yang, Fen Fang, Ying Sun, Joo-Hwee Lim, Hongyuan Zhu

AI总结 本文提出基于贝叶斯决策理论的后处理校准框架，通过可学习标量修正提升AI生成图像检测的鲁棒性，无需重新训练。

Comments AAAI 2026. Code: https://github.com/muliyangm/AIGI-Det-Calib

Unmediated AI-Assisted Scholarly Citations

无中介的人工智能辅助学术引用

Stefan Szeider

AI总结 本文提出一种结合语言模型与直接数据库访问的架构，通过模型上下文协议实现准确的学术引用处理，提升文献检索的可靠性和效率。

Journal ref Open Conference Proceedings, Vol. 8 (2026): The Second Bridge on Artificial Intelligence for Scholarly Communication (AAAI-26)

A Lightweight Sparse Interaction Network for Time Series Forecasting

一种轻量级稀疏交互网络用于时间序列预测

Xu Zhang, Qitong Wang, Peng Wang, Wei Wang

AI总结 本文提出了一种轻量级稀疏交互网络LSINet，通过稀疏性诱导的伯努利分布学习时间步之间的关键连接，以提升时间序列预测的准确性和效率。

Comments The paper is published in AAAI Conference on Artificial Intelligence, AAAI 2025. The code is available at the link https://github.com/Meteor-Stars/LSINet

SAPO: Self-Adaptive Process Optimization Makes Small Reasoners Stronger

SAPO：自适应过程优化使小型推理器更强

Kaiyuan Chen, Guangmin Zheng, Jin Wang, Xiaobing Zhou, Xuejie Zhang

AI总结 SAPO通过自适应过程优化方法，有效缩小推理器与验证器之间的差距，提升小型语言模型在数学和代码任务中的性能。

Comments Accepted by AAAI 2026

CrossCheck-Bench: Diagnosing Compositional Failures in Multimodal Conflict Resolution

CrossCheck-Bench：多模态冲突解决中的组成性故障诊断

Baoliang Tian, Yuxuan Si, Jilong Wang, Lingyao Li, Zhongyuan Bao, Zineng Zhou, Tao Wang, Sixu Li, Ziyao Xu, Mingze Wang, Zhouzhuo Zhang, Zhihao Wang, Yike Yun, Ke Tian, Ning Yang, Minghui Qiu

AI总结 CrossCheck-Bench通过多阶段注释流程构建的基准测试，揭示了多模态模型在处理跨模态冲突时的瓶颈，并表明融合符号推理与视觉处理的方法能提升模型的稳健性。

Comments Accepted by AAAI 2026

Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching

通过一比特随机草图实现双向通信压缩的个性化联邦学习

Jiacheng Cheng, Xu Zhang, Guanghui Qiu, Yifang Zhang, Yinchuan Li, Kaiyuan Feng

AI总结 本文提出pFed1BS框架，通过一比特随机草图实现个性化联邦学习中的通信压缩，结合符号正则化和快速哈达玛变换，有效降低通信成本并保持性能

Comments Accepted in AAAI 2026

DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening

DeepGB-TB: 一种风险平衡的跨注意力梯度提升卷积网络用于快速、可解释的肺结核筛查

Zhixiang Lu, Yulong Li, Feilong Tang, Zhengyong Jiang, Chong Li, Mian Zhou, Tenglong Li, Jionglong Su

AI总结 DeepGB-TB通过结合跨注意力机制和梯度提升决策树，实现快速、可解释的肺结核筛查，具有高准确率和低资源需求。

Comments Accepted by AAAI 2026 (oral)

Journal ref Proceedings of the AAAI Conference on Artificial Intelligence, 2026

KVmix: Gradient-Based Layer Importance-Aware Mixed-Precision Quantization for KV Cache

KVmix: 基于梯度的层重要性感知的KV缓存混合精度量化

Fei Li, Song Liu, Weiguo Wu, Shiqiang Nie, Jinyu Wang

机构 * Fei Li, Song Liu, Weiguo Wu, Shiqiang Nie, Jinyu Wang（作者） 纠错

AI总结 KVmix通过基于梯度的重要性分析实现KV缓存的混合精度量化，有效平衡精度与效率，实现低内存高吞吐的LLM推理

Comments AAAI 2026 Oral

Minimizing Inequity in Facility Location Games

在设施选址游戏中最小化不平等

Yuhang Guo, Houyu Zhou

AI总结 本文提出两种新的机制以最小化设施选址游戏中群体效应，统一了多个经典诚实机制，并改进了群体公平目标的近似界限。

Comments Accepted in AAAI 2026

CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation

CoRe-Fed: 通过联邦嵌入蒸馏实现协同与表示公平性

Noorain Mukhtiar, Adnan Mahmood, Quan Z. Sheng

AI总结 CoRe-Fed通过联邦嵌入蒸馏实现协同与表示公平性，通过嵌入级正则化和公平性感知聚合策略提升模型性能和公平性。

Comments 7 pages (main content), 2 pages (references), Accepted in AAAI 2026

Kernelized Edge Attention: Addressing Semantic Attention Blurring in Temporal Graph Neural Networks

核化边注意力：解决时间图神经网络中语义注意力模糊问题

Govind Waghmare, Srini Rohan Gujulla Leel, Nikhil Tumbde, Sumedh B G, Sonia Gupta, Srikanta Bedathur

AI总结 KEAT通过核化边注意力机制提升时间图神经网络对时间依赖性的捕捉能力，实现更准确和可解释的信息传递。

Comments Accepted at AAAI 2026

Reinforced Rate Control for Neural Video Compression via Inter-Frame Rate-Distortion Awareness

通过帧间速率-失真意识的强化率控实现神经视频压缩

Wuyang Cong, Junqi Shi, Lizhong Wang, Weijing Shi, Ming Lu, Hao Chen, Zhan Ma

AI总结 本文提出基于强化学习的神经视频压缩率控方法，通过联合优化比特率分配和编码参数，实现更高效的压缩性能和更低的开销。

Comments Accepted by AAAI 2026

PSM: Prompt Sensitivity Minimization via LLM-Guided Black-Box Optimization

PSM：通过LLM引导的黑盒优化实现提示敏感性最小化

Huseein Jawad, Nicolas Brunel

AI总结 本文提出PSM框架，通过LLM引导的黑盒优化，利用SHIELD层减少系统提示泄露，提升LLM安全性和隐私保护。

Comments accepted at the Trustworthy Agentic AI Workshop @ AAAI 2026 (Singapore, Jan 27, 2026). Workshop PDF: https://trustagenticai.github.io/AAAI2026/AAAI-Workshop/20.pdf

Efficient Thought Space Exploration Through Strategic Intervention

通过战略干预实现高效思维空间探索

Ziheng Li, Hengyi Cai, Xiaochi Wei, Yuchen Li, Shuaiqiang Wang, Zhi-Hong Deng, Dawei Yin

机构 * Baidu（百度） 纠错

AI总结 HPR框架通过战略干预减少计算成本，在保持高准确率的同时实现高效推理。

Comments AAAI 2026

Beyond Cosine Similarity: Magnitude-Aware CLIP for No-Reference Image Quality Assessment

超越余弦相似度：面向无参考图像质量评估的幅度感知CLIP

Zhicheng Liao, Dongxu Wu, Zhenshan Shi, Sijie Mai, Hanwei Zhu, Lingyu Zhu, Yuncheng Jiang, Baoliang Chen

AI总结 本文提出一种基于CLIP的无参考图像质量评估方法，通过引入幅度感知的特征处理和置信度引导的融合机制，提升图像质量评估的准确性。

Comments Accepted by AAAI 2026

Enhancing Human-Like Responses in Large Language Models

增强大型语言模型的人类样响应

Ethem Yağız Çalık, Talha Rüzgar Akkuş

AI总结 本文提出通过提升自然语言理解、对话连贯性和情感智能来增强大型语言模型的人类化响应，展示了改进用户交互和跨领域应用的潜力。

Comments Presented at the AAAI-26 Workshop on Personalization in the Era of Large Foundation Models (PerFM), Singapore, January 2026

Journal ref Presented at the AAAI-26 Workshop on Personalization in the Era of Large Foundation Models (PerFM), 2026

Generalized dynamic cognitive hierarchy models for strategic driving behavior

通用动态认知层次模型用于战略驾驶行为

Atrisha Sarkar, Kate Larson, Krzysztof Czarnecki

AI总结 本文提出通用动态认知层次模型，用于建模自然驾驶行为和自动驾驶车辆的行为规划，通过自动机策略和稳健响应解决有限理性和共同知识假设问题。

Journal ref Proceedings of the AAAI Conference on Artificial Intelligence, 36(5), 5173-5182 (2022)

Enhancing Logical Expressiveness in Graph Neural Networks via Path-Neighbor Aggregation

通过路径邻居聚合增强图神经网络的逻辑表达能力

Han Yu, Xiaojuan Zhao, Aiping Li, Kai Chen, Ziniu Liu, Zhichao Peng

AI总结 本文提出PN-GNN方法，通过路径邻居聚合提升图神经网络在知识图谱中的逻辑表达能力，理论分析和实验验证其优于现有方法。

Comments Accepted to AAAI 2026

Breaking the Adversarial Robustness-Performance Trade-off in Text Classification via Manifold Purification

通过流形净化打破文本分类中的对抗鲁棒性-性能权衡

Chenhao Dang, Jing Ma

AI总结 本文提出MC^2F方法，通过流形净化技术提升文本分类在对抗攻击下的鲁棒性，同时保持干净数据性能。

Comments 9 pages,3 figures, AAAI 2026 Poster

Posterior Label Smoothing for Node Classification

后验标签平滑用于节点分类

Jaeseung Heo, Moonjeong Park, Dongwoo Kim

AI总结 本文提出后验标签平滑方法，通过后验分布生成软标签以提升图结构数据中节点分类的准确性和泛化能力。

Comments Accepted by AAAI 2026

Note2Chat: Improving LLMs for Multi-Turn Clinical History Taking Using Medical Notes

Note2Chat: 通过医疗笔记提升LLM在多轮临床病史采集中的表现

Yang Zhou, Zhenting Sheng, Mingrui Tan, Yuting Song, Jun Zhou, Yu Heng Kwan, Lian Leng Low, Yang Bai, Yong Liu

AI总结 Note2Chat通过医疗笔记训练LLM提升多轮临床病史采集的准确性和效率

Comments Accepted at AAAI-26

LAMP: Learning Universal Adversarial Perturbations for Multi-Image Tasks via Pre-trained Models

LAMP: 通过预训练模型学习通用对抗扰动以实现多图像任务

Alvi Md Ishmam, Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Chris Thomas

AI总结 LAMP通过预训练模型学习通用对抗扰动，针对多图像多模态大语言模型实现高效的黑盒攻击，提升了多任务攻击成功率。

Comments Accepted in main technical track AAAI 2026