Currently, prompting techniques can be mainly divided into two categories:1)shot method implicitly inspires the model to answer the question by mimicing the steps in the given example, e.g., the few-shot CoT. 2) Guideline method explicitly instructs the model to reason by following guidelines, which contains succinct and concise task-specific knowledge. Shot method is prone to difficulties in terms of selection of shots type, the number of shots, and the design of the reasoning steps, so a question arises: can we only use guideline instead of shot in the prompt? To this end, we propose the FGT framework to automatically learn task-specific guidelines from dataset consisting of Feedback, Guideline, and Tree-gather agents. First, the feedback agent is designed to evaluate the outcomes, both right and wrong, of each Q&A to gather insights guiding more effective optimization strategies. Next, the guideline agent is tasked with deriving guidelines from each piece of feedback and storing them in local memory. Lastly, the tree-gather agent aggregates all guidelines hierarchically through a tree structure, ultimately obtaining all unduplicated guidelines from a global perspective. In addition, we induce the model to generate intermediate processes to ensure the reasoning consistent with the guidelines. Experimental results demonstrate that our approach achieves superior performance across multiple tasks, thereby highlighting the effectiveness of using the guidelines in prompt.

We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications. This Visual IDE prioritizes both the Integrity of its development tools and the Visuality of its components, ensuring a smooth and efficient building experience.On one hand, AI2Apps integrates a comprehensive development toolkit ranging from a prototyping canvas and AI-assisted code editor to agent debugger, management system, and deployment tools all within a web-based graphical user interface. On the other hand, AI2Apps visualizes reusable front-end and back-end code as intuitive drag-and-drop components. Furthermore, a plugin system named AI2Apps Extension (AAE) is designed for Extensibility, showcasing how a new plugin with 20 components enables web agent to mimic human-like browsing behavior. Our case study demonstrates substantial efficiency improvements, with AI2Apps reducing token consumption and API calls when debugging a specific sophisticated multimodal agent by approximately 90% and 80%, respectively. The AI2Apps, including an online demo, open-source code, and a screencast video, is now publicly accessible.

A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assumption may not hold in real-world scenarios. In this paper, we consider the problem of coresets construction over relational data. Namely, the data is decoupled into several relational tables, and it could be very expensive to directly materialize the data matrix by joining the tables. We propose a novel approach called ``aggregation tree with pseudo-cube'' that can build a coreset from bottom to up. Moreover, our approach can neatly circumvent several troublesome issues of relational learning problems [Khamis et al., PODS 2019]. Under some mild assumptions, we show that our coreset approach can be applied for the machine learning tasks, such as clustering, logistic regression and SVM.