Retrieval-Augmented Generation (RAG) has become a foundational paradigm for integrating AI agents with external knowledge. However, current RAG models remain largely constrained by static retrieval pipelines and limited capacity for adaptive reasoning over hierarchical knowledge structures. As AI agents increasingly operate in dynamic, information-rich environments, there is a growing need for models that can reason across modalities while continuously evolving their knowledge representations. We introduce HFlow, a self-evolving multimodal agentic RAG framework grounded in hypergraph representations and hyperbolic flow-based reasoning. In our formulation, heterogeneous modalities, including text, images, audio, and structured data, are modelled as nodes within a multimodal hypergraph, while hyperedges capture higher-order semantic and cross-modal relationships. By embedding this structure in hyperbolic space, the framework preserves hierarchical and compositional knowledge. This work proposes a shift from static retrieval to continuous knowledge navigation, where reasoning emerges through geometry-aware traversal of multimodal knowledge manifolds. The proposed framework unifies retrieval, reasoning, and adaptation within a single agentic architecture, offering a new direction for scalable, context-aware AI models. We discuss early empirical evidence demonstrating improved robustness and reasoning flexibility compared to conventional Euclidean and pipeline-based multimodal RAG approaches and outline future opportunities for self-improving knowledge agents.

Research on Information Retrieval (IR) has historically prioritised high-resource languages such as English and Chinese, with less attention given to many low-resource languages. For example, Shona, a Bantu language spoken by approximately 12 million people in Zimbabwe and neighbouring countries, remains under-explored in IR research despite its widespread societal use in Southern Africa. In this work, we present a preliminary study of Shona IR using sparse and dense retrieval models, demonstrating significant performance limitations due to morphological complexity and data scarcity. Based on these findings, we propose to develop a framework to advance Shona IR by developing a large-scale benchmark dataset to support morphology-aware retrieval. We hypothesise that improving Shona IR supports equitable access to digital information and enables language-inclusive AI technologies aligned with global development priorities such as accessibility to education, dissemination of healthcare information, and digital inclusion.

\beginabstract Recently, modelling heterogeneous interactions in recommender systems has attracted research interest. Real-world scenarios involve sequential multi-type user-item interactions such as ''shape view'', ''shape add-to-favourites'', ''shape add-to-cart'' and ''shape purchase''. Graph Neural Network (GNN) methods have been widely adopted in Representation Learning of similar sequential user-item interactions. Promising results have been achieved by the integration of GNNs and transformers for self-attention. However, GNN based methods suffer from limited capability in handling global user-item interaction dependencies, particularly for long sequences. Moreover, these models require high computational cost of transformers, due to the quadratic memory and time complexity with respect to sequence length. This results in memory bottlenecks and slow training especially in computational resource-constrained environments. To address these challenges, we propose the FATH model which employs Flash Attention mechanism to reduce the high-bandwidth memory usage over higher-order user-item interaction sequences. Experimental results show that our model improves the training speed and reduces the memory usage with better recommendation performance in comparison with the state-of the art baselines.

This paper discusses the current challenges in modeling real world recommendation scenarios and proposes the development of a unified Heterogeneous Graph Representation Learning framework for multi-target Cross-Domain recommendation (HGRL4CDR). A shared graph with user-item interactions from multiple domains is proposed as a way to provide an effective representation learning layer and unify the modelling of various heterogeneous data. A heterogeneous graph transformer network will be integrated to the representation learning model to prioritize the most important neighbours, and the proposed model would be able to capture complex information as well as adapt to dynamic changes in the data using matrix perturbation. Using the real world Amazon Review dataset, experiments would be conducted on multi-target cross domain recommendation.