AAAI

X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and A Novel LLM-consulted Explanation Framework

X-MuTeST：一个用于可解释仇恨言论检测的多语言基准及一种新颖的LLM咨询解释框架

Mohammad Zia Ur Rehman, Sai Kartheek Reddy Kasu, Shashivardhan Reddy Koppula, Sai Rithwik Reddy Chirra, Shwetank Shekhar Singh, Nagendra Kumar

机构 * Indian Institute of Technology Indore（印度理工学院Indore） ； Indian Institute of Information Technology Dharwad（印度信息科技学院Dharwad） ； Arizona State University（亚利桑那州立大学） ； Indian Institute of Technology Mandi（印度理工学院Mandi） 纠错

AI总结 X-MuTeST提出一种结合LLM和传统技术的多语言仇恨言论检测框架，通过人类注释的解释提升分类性能和可解释性。

Comments Accepted in the proceedings of AAAI 2026

Journal ref AAA 2026 (AISI)

DiffBench Meets DiffAgent: End-to-End LLM-Driven Diffusion Acceleration Code Generation

DiffBench 与 DiffAgent：端到端的 LLM 驱动扩散加速代码生成

Jiajun jiao, Haowei Zhu, Puyuan Yang, Jianghui Wang, Ji Liu, Ziqiong Liu, Dong Li, Yuejian Fang, Junhai Yong, Bin Wang, Emad Barsoum

AI总结 本文提出DiffBench和DiffAgent，通过LLM驱动的闭环流程，实现端到端的扩散模型加速代码生成，显著提升生成策略的有效性。

Comments Accepted to AAAI 2026

AbductiveMLLM: Boosting Visual Abductive Reasoning Within MLLMs

AbductiveMLLM: 提升多模态大语言模型中的视觉归纳推理

Boyu Chang, Qi Wang, Xi Guo, Zhixiong Nan, Yazhou Yao, Tianfei Zhou

AI总结 AbductiveMLLM通过结合REASONER和IMAGINER组件，提升多模态大语言模型在视觉归纳推理中的性能。

Comments Accepted by AAAI 2026 as Oral. Code:https://github.com/ChangPtR/AbdMLLM

Inferring Causal Graph Temporal Logic Formulas to Expedite Reinforcement Learning in Temporally Extended Tasks

推断因果图时序逻辑公式以加速时序扩展任务中的强化学习

Hadi Partovi Aria, Zhe Xu

AI总结 GTL-CIRL通过同时学习策略和挖掘因果图时序逻辑规范，加速时序扩展任务中的强化学习，提升学习效率和行为可验证性。

Comments Accepted to AAAI-26 Bridge Program B10: Making Embodied AI Reliable with Testing and Formal Verification

LendNova: Towards Automated Credit Risk Assessment with Language Models

LendNova：迈向利用语言模型进行自动信用风险评估

Kiarash Shamsi, Danijel Novokmet, Joshua Peters, Mao Lin Liu, Paul K Edwards, Vahab Khoshdel

AI总结 LendNova利用语言模型实现自动信用风险评估，通过直接处理原始信用记录文本，无需手动特征工程，提高准确性和可扩展性。

Journal ref AAAI 2026, Workshop on Agentic AI in Financial Services

Spiking Heterogeneous Graph Attention Networks

脉冲异构图注意力网络

Buqing Cao, Qian Peng, Xiang Xie, Liang Chen, Min Shi, Jianxun Liu

AI总结 SpikingHAN通过融合脉冲神经网络的节能特性，降低异构图学习的计算成本，同时保持性能。

Comments This paper has been accepted by AAAI 2026

Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases

Thucy：基于LLM的多智能体系统，用于跨关系数据库的声明验证

Michael Theologitis, Dan Suciu

AI总结 Thucy是一种基于LLM的多智能体系统，能够跨关系数据库验证声明，并提供支持裁定的SQL查询，准确率达94.3%。

Comments Accepted at AAAI 2026 Workshop on LLM-based Multi-Agent Systems (LaMAS)