The recent developments in the field of dense retrieval and information retrieval have shown a significant focus on improving efficiency and effectiveness through novel methodologies and techniques. One prominent direction is the exploration of dimensionality reduction methods that enhance retrieval efficiency without compromising effectiveness. These methods often leverage matrix decomposition and static pruning techniques, which can be executed offline, thereby reducing computational overhead during query processing. Another area of interest is the enhancement of feature selection methods, particularly for high-dimensional datasets, where new approaches are being developed to improve classification performance by selecting features based on class-specific models. Additionally, advancements in top-k threshold estimation have been made, with researchers proposing enhancements to existing quantile-based methods to achieve better estimates, especially for learned sparse index structures. The field is also grappling with the challenge of adversarial hubness in multi-modal retrieval, where new methods are being investigated to create and mitigate adversarial hubs that can affect retrieval accuracy. Lastly, the concept of leveraging pseudo-irrelevance feedback for dense retrieval has gained traction, with new methodologies being proposed to enhance retrieval performance by estimating noisy dimensions in relevant documents. Overall, these developments indicate a shift towards more robust and efficient retrieval systems that can handle complex data and adversarial scenarios.