AAAI

ShapBPT: Image Feature Attributions Using Data-Aware Binary Partition Trees

ShapBPT：基于数据感知二进制划分树的图像特征归因

Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari, Damiano Verda

AI总结 本文提出ShapBPT，一种基于层次Shapley公式的数据感知图像可视化方法，通过多尺度二进制划分树结构提升图像特征归因的效率与语义意义。

Comments Presented at AAAI-26 conference and published in Proceedings of the The Fortieth AAAI Conference on Artificial Intelligence (AAAI-26)

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

A Machine Learning Approach for Detection of Mental Health Conditions and Cyberbullying from Social Media

从社交媒体检测心理健康状况和网络欺凌的一种机器学习方法

Edward Ajayi, Martha Kachweka, Mawuli Deku, Emily Aiken

机构 * Carnegie Mellon University Africa Kigali, Rwanda（卡内基梅隆大学非洲分校基加利分校，刚果（金）） 纠错

AI总结 本文提出统一的多类分类框架，用于从社交媒体数据中检测十种不同的心理健康和网络欺凌类别，通过实验表明端到端微调对性能至关重要，MentalBERT模型在准确率和宏F1分数上表现优异。

Comments Best Paper Award at the AAAI-26 Bridge Program on AI for Medicine and Healthcare. Published in Proceedings of the Second AAAI Bridge Program on AI for Medicine and Healthcare, PMLR 317:15-26, 2026. Paper URL: https://proceedings.mlr.press/v317/ajayi26a.html

GraphIF: Enhancing Multi-Turn Instruction Following for Large Language Models with Relation Graph Prompt

GraphIF: 通过关系图提示增强大语言模型的多轮指令遵循

Zhenhe Li, Can Lin, Ling Zheng, Wen-Da Wei, Junli Liang, Qi Song

机构 * Zhenhe Li 1（李振和1） ； Can Lin 1（林灿1） ； Ling Zheng 1（郑凌1） ； Wen-Da Wei 2（韦文达2） ； Junli Liang 1（梁俊利1） ； Qi Song 1（宋琪1） 纠错

AI总结 GraphIF通过构建关系图结构，利用图提示提升大语言模型的多轮指令遵循能力，实验表明其在多轮对话评估指标上表现优异。

Journal ref Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2026)

Schema on the Inside: A Two-Phase Fine-Tuning Method for High-Efficiency Text-to-SQL at Scale

内部架构：一种两阶段微调方法，用于大规模高效文本到SQL

Chinmay Soni, Shivam Chourasia, Gaurav Kumar, Hitesh Kapoor

AI总结 本文提出一种两阶段监督微调方法，使模型能内部化整个数据库架构，减少输入token至100以下，提升文本到SQL的精度和效率。

Comments 8 pages, 6 figures. Published in the Proceedings of the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), 2026

Journal ref Proceedings of the AAAI Conference on Artificial Intelligence 40(47) (2026) 40110-40117

AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition

AutoSCORE: 通过结构化组件识别提升多智能体大语言模型的自动评分

Yun Wang, Zhaojun Ding, Xuansheng Wu, Siyue Sun, Ninghao Liu, Xiaoming Zhai

AI总结 AutoSCORE通过多智能体大语言模型和结构化组件识别提升自动评分的准确性与可解释性，在多个基准数据集上表现出色，尤其在复杂评分标准下效果显著。

Comments 9 pages, 2 figures

Journal ref Proceedings of the AAAI Conference on Artificial Intelligence, 40(48), 40898-40906, 2026

SafeSieve: From Heuristics to Experience in Progressive Pruning for LLM-based Multi-Agent Communication

SafeSieve: 从启发式到经验在基于大语言模型的多智能体通信中的渐进剪枝

Ruijia Zhang, Xinyan Zhao, Ruixiang Wang, Sigen Chen, Guibin Zhang, An Zhang, Kun Wang, Qingsong Wen

AI总结 本文提出SafeSieve，一种渐进适应的多智能体剪枝算法，通过双重机制动态优化智能体间通信。实验显示其在多个基准上实现了高准确率并显著降低token使用量，同时在异构环境下保持性能。

Comments AAAI-2026 poster; 7 pages for main content, 5 figures, 4 tables