Tootfinder

GPR: Empowering Generation with Graph-Pretrained Retriever
Xiaochen Wang, Zongyu Wu, Yuan Zhong, Xiang Zhang, Suhang Wang, Fenglong Ma
https://arxiv.org/abs/2506.00261

GPR: Empowering Generation with Graph-Pretrained Retriever
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. GPR aligns natural language questions with relevant subgraphs through LLM-guided graph augmentation and employs a structure-aware…

Fodor and Pylyshyn's Legacy - Still No Human-like Systematic Compositionality in Neural Networks
Tim Woydt, Moritz Willig, Antonia W\"ust, Lukas Helff, Wolfgang Stammer, Constantin A. Rothkopf, Kristian Kersting
https://arxiv.org/abs/2506.01820

Fodor and Pylyshyn's Legacy - Still No Human-like Systematic Compositionality in Neural Networks
Strong meta-learning capabilities for systematic compositionality are emerging as an important skill for navigating the complex and changing tasks of today's world. However, in presenting models for robust adaptation to novel environments, it is important to refrain from making unsupported claims about the performance of meta-learning systems that ultimately do not stand up to scrutiny. While Fodor and Pylyshyn famously posited that neural networks inherently lack this capacity as they are unab…