We propose RicciFlowRec, a geometric recommendation framework that performs root cause attribution via Ricci curvature and flow on dynamic financial graphs.By modelling evolving interactions among stocks, macroeconomic indicators, and news, we quantify local stress using discrete Ricci curvature and trace shock propagation via Ricci flow.Curvature gradients reveal causal substructures, informing a structural risk-aware ranking function.Preliminary results on S&P 500 data with FinBERT-based sentiment show improved robustness and interpretability under synthetic perturbations.This ongoing work supports curvature-based attribution and early-stage risk-aware ranking, with plans for portfolio optimization and return forecasting.To our knowledge, RicciFlowRec is the first recommender to apply geometric flow-based reasoning in financial decision support.

Integrating transformers with graph representation learning has emerged as a research focal point. However, recent studies showed that positional encoding in Transformers does not capture enough structural information between nodes. Additionally, existing graph neural network (GNN) models face the oversquashing issue, impeding information retention from distant nodes. To address, we transform graphs into regular structures, such as tokens, to enhance positional understanding and leverage transformer strengths. Inspired by the visual transformer (ViT) model, we propose partitioning graphs into patches and apply GNN models obtain fixed size vectors. Notably, our approach adopts contrastive learning for in-depth graph structure and incorporate more topological information via Ricci curvature to alleviate over-squashing problem by attenuating the effects of negatively curved edges while preserving the original graph structure. Unlike existing graph rewiring methods that directly modify graph structure by adding or removing edges, this approach is potentially more suitable for applications such as molecular learning where structural preservation is important. Our innovative pipeline subsequently introduces the PerformerMixer, a transformer variant with linear complexity, ensuring efficient computation. Evaluations on real-world benchmarks demonstrate our framework’s superior performance, like Peptides-func and achieve 3-WL expressiveness.

Medical visual question answering (Med-VQA) is to answer medical questions based on clinical images provided. This field is still in its infancy due to the complexity of the trio formed of questions, multimodal features and expert knowledge. In this paper, we tackle, a ’myth’ in the Natural Language Processing area - that unimodal bias is always considered undesirable in learning models. Additionally, we study the effect of integrating a novel dynamic attention mechanism into such models, inspired by a recent graph deep learning study.Unlike traditional attention, dynamic attention scores are conditioned on different query words in a question and thus enhance the representation learning ability of texts. We propose that some questions are answered more accurately with a reinforcement of question embedding after fusing multimodal features. Extensive experiments have been implemented on the VQA-RAD datasets and demonstrate that our proposed model, reinforCe unimOdal dynamiC Attention (COCA), outperforms the state-of-the-art methods overall and performs competitively at open-ended question answering.