arXiv subjects

Xianyao Ling

Publications and source records attributed to Xianyao Ling.

4 recordsLinked to original sources

arXiv2026

Agentic Nesting: A New Methodology for Existing Enterprise Application Integration and Services

Enterprise operations extensively rely on multiple heterogeneous business systems and information applications, which also result in severe data silos and process fragmentation. Enterprises have invested considerable financial and material resources in building these applications, however, effectively leveraging and orchestrating them remains a formidable challenge. Conventional approaches to enterprise application integration, encompassing middleware architectures such as Enterprise Service Bus (ESB), API gateway infrastructures, and Robotic Process Automation (RPA), suffer from inherent limitations like high architectural coupling, escalating operation and maintenance costs, and limited intelligence capabilities. This paper proposes Agentic Nesting, a multi-agent collaboration framework in which existing enterprise applications are encapsulated as autonomous AI agents within a hierarchically nested structure. Rather than flat interconnection, agents are organized into layered stewardship topologies that mirror the compositional complexity of enterprise ecosystems. The framework extracts a digital agent proxy from each legacy application to enable natural-language interaction and autonomous manipulation, coordinates multiple agents through a central orchestrator for task decomposition and dynamic dispatching, and exposes a unified conversational interface for cross-application querying and process orchestration. The main contributions of this paper are the proposition of the "Application-as-Agent" integration paradigm and the "Conversation-as-Integration" interaction philosophy, together with an exploration of the generalization potential of this methodology in scenarios encompassing heterogeneous system coordination, and large-scale data applications.

cs.AI↗

arXiv2025

LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment

The rapid progress in Generative AI and Agent technologies is profoundly transforming enterprise data management and analytics. Traditional database applications and system deployment are fundamentally impacted by AI-driven tools, such as Retrieval-Augmented Generation (RAG) and vector database technologies, which provide new pathways for semantic querying over enterprise knowledge bases. In the meantime, data security and compliance are top priorities for organizations adopting AI technologies. For enterprise data analysis, SQL generations powered by large language models (LLMs) and AI agents, has emerged as a key bridge connecting natural language with structured data, effectively lowering the barrier to enterprise data access and improving analytical efficiency. This paper focuses on enterprise data analysis applications and system deployment, covering a range of innovative frameworks, enabling complex query understanding, multi-agent collaboration, security verification, and computational efficiency. Through representative use cases, key challenges related to distributed deployment, data security, and inherent difficulties in SQL generation tasks are discussed.

cs.DB↗

arXiv2025

Multi-dimensional Data Analysis and Applications Basing on LLM Agents and Knowledge Graph Interactions

In the current era of big data, extracting deep insights from massive, heterogeneous, and complexly associated multi-dimensional data has become a significant challenge. Large Language Models (LLMs) perform well in natural language understanding and generation, but still suffer from "hallucination" issues when processing structured knowledge and are difficult to update in real-time. Although Knowledge Graphs (KGs) can explicitly store structured knowledge, their static nature limits dynamic interaction and analytical capabilities. Therefore, this paper proposes a multi-dimensional data analysis method based on the interactions between LLM agents and KGs, constructing a dynamic, collaborative analytical ecosystem. This method utilizes LLM agents to automatically extract product data from unstructured data, constructs and visualizes the KG in real-time, and supports users in deep exploration and analysis of graph nodes through an interactive platform. Experimental results show that this method has significant advantages in product ecosystem analysis, relationship mining, and user-driven exploratory analysis, providing new ideas and tools for multi-dimensional data analysis.

cs.AI↗

arXiv2023

Optimizing and Fine-tuning Large Language Model for Urban Renewal

This study aims to innovatively explore adaptive applications of large language models (LLM) in urban renewal. It also aims to improve its performance and text generation quality for knowledge question-answering (QA) tasks. Based on the ChatGLM, we automatically generate QA datasets using urban renewal scientific literature corpora in a self-instruct manner and then conduct joint fine-tuning training on the model using the Prefix and LoRA fine-tuning methods to create an LLM for urban renewal. By guiding the LLM to automatically generate QA data based on prompt words and given text, it is possible to quickly obtain datasets in the urban renewal field and provide data support for the fine-tuning training of LLMs. The experimental results show that the joint fine-tuning training method proposed in this study can significantly improve the performance of LLM on the QA tasks. Compared with LoRA fine-tuning, the method improves the Bleu and Rouge metrics on the test by about 5%; compared with the model before fine-tuning, the method improves the Bleu and Rouge metrics by about 15%-20%. This study demonstrates the effectiveness and superiority of the joint fine-tuning method using Prefix and LoRA for ChatGLM in the urban renewal knowledge QA tasks. It provides a new approach for fine-tuning LLMs on urban renewal-related tasks.

cs.CL↗