Vortex under Ripplet: An Empirical Study of RAG-enabled Applications

Large language models (LLMs) enhanced by retrieval-augmented generation (RAG) provide effective solutions in various application scenarios. However, developers face challenges in integrating RAG-enhanced LLMs into software systems, due to lack of interface specification, requirements from software context, and complicated system management. In this paper, we manually studied 100 open-source applications that incorporate RAG-enhanced LLMs, and their issue reports. We have found that more than 98% of applications contain multiple integration defects that harm software functionality, efficiency, and security. We have also generalized 19 defect patterns and proposed guidelines to tackle them. We hope this work could aid LLM-enabled software development and motivate future research.

Implicit Euler ODE Networks for Single-Image Dehazing

Deep convolutional neural networks (CNN) have been applied for image dehazing tasks, where the residual network (ResNet) is often adopted as the basic component to avoid the vanishing gradient problem. Recently, many works indicate that the ResNet can be considered as the explicit Euler forward approximation of an ordinary differential equation (ODE). In this paper, we extend the explicit forward approximation to the implicit backward counterpart, which can be realized via a recursive neural network, named IM-block. Given that, we propose an efficient end-to-end multi-level implicit network (MI-Net) for the single image dehazing problem. Moreover, multi-level fusing (MLF) mechanism and residual channel attention block (RCA-block) are adopted to boost performance of our network. Experiments on several dehazing benchmark datasets demonstrate that our method outperforms existing methods and achieves the state-of-the-art performance.