KDD 2026论文提出Tokenized MoE框架，用专家混合机制突破GNN泛化瓶颈
arXiv:2602.09258v2 Announce Type: replace Abstract: Deployed graph neural networks (GNNs) are frozen at deployment yet must fit clean data, generalize…
KDD 2026论文：用语义ID推理提升生成式推荐，新思路值得关注
arXiv:2603.23183v2 Announce Type: replace-cross Abstract: Recent advances in generative recommendation have leveraged pretrained LLMs by formulating s…
KDD 2026新研究，对比SHAP、规则提取与RuleSHAP三种全局可解释方法，检测大模型中注入的误导信息行为。
arXiv:2505.11189v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can amplify misinformation, undermining societal goals such as …
联邦学习与大语言模型交叉领域的最新综述，由PAKDD 2026接收，系统梳理进展并展望未来方向。
arXiv:2409.15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their …
通过约束微调中的安全token，在极少量参数变动下保住大模型的安全护栏，KDD 2026 新思路。
arXiv:2603.07445v2 Announce Type: replace Abstract: Large language models (LLMs) often require fine-tuning (FT) to perform well on downstream tasks, b…
顶会KDD 2026提出HyperPatch，解决大模型在n元结构漂移下的顺序知识编辑难题，新范式值得关注
arXiv:2606.03179v1 Announce Type: new Abstract: Large Language Models (LLMs) rely on Knowledge Editing (KE) to maintain temporal validity, yet real-wo…
多智能体系统故障溯源难？基于时间语义的新框架StepFinder，被KDD 2026接收，帮系统快速定位协作失败根源。
arXiv:2606.03467v1 Announce Type: new Abstract: LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step task…
KDD 2026论文提出针对纵向数据中罕见事件的可扩展反事实风险估计方法，解决因果推断与高维数据挑战。
arXiv:2606.01539v1 Announce Type: cross Abstract: Estimating the causal effect of time-varying treatments on survival outcomes in large observational …
用图大语言模型破解药物协同预测的分布外泛化难题，KDD 2026新方法。
arXiv:2605.30247v1 Announce Type: new Abstract: Drug synergy prediction (DSP) aims to identify efficacious drug combinations under various cellular co…
KDD 2026论文系统梳理因果推断在LLM开发与评估中的前沿方法，实操价值高。
arXiv:2605.25998v1 Announce Type: new Abstract: Large language model (LLM) development is currently driven by large-scale empirical iteration over dat…
大模型驱动自动特征工程，协作贝叶斯超参数优化提效，KDD 2026前沿方法。
arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottlenec…
首个针对量化回测的LLM基准测试，被KDD 2026录用，评估自动策略生成能力。
arXiv:2605.17937v1 Announce Type: new Abstract: Quantitative backtesting is essential for evaluating trading strategies but remains hampered by high t…