arXiv subjects

Jinpeng Lv

Publications and source records attributed to Jinpeng Lv.

2 recordsLinked to original sources

arXiv2026

AccountAgent: AI Accounting Assistant System

The AI Accounting Assistant System is an innovative tool that improves the accuracy and efficiency of financial management and is becoming a core support for enterprise accounting. It relies on machine learning, natural language processing, and data visualization to automate the full accounting agent including bookkeeping, report generation, and data analysis, substantially reducing manual operations and minimizing human error. Designed to resolve the pain points of low efficiency, cumbersome workflows, and data lag in traditional accounting, the system shifts financial work from repetitive labor toward high-value decision support. It deeply mines historical financial data, precisely identifies operating trends, and provides real-time, targeted insight for strategic planning, risk prevention, and operating decisions. By reconstructing the accounting agent, the system realizes automated bookkeeping, intelligent analysis, and efficient compliance, driving the accounting profession from a bookkeeping orientation toward a management orientation. This document presents the architecture, methodology, key algorithms, and functional modules of the platform.

cs.CE↗

arXiv2024

Unlocking Insights: Semantic Search in Jupyter Notebooks

Semantic search, a process aimed at delivering highly relevant search results by comprehending the searcher's intent and the contextual meaning of terms within a searchable dataspace, plays a pivotal role in information retrieval. In this paper, we investigate the application of large language models to enhance semantic search capabilities, specifically tailored for the domain of Jupyter Notebooks. Our objective is to retrieve generated outputs, such as figures or tables, associated functions and methods, and other pertinent information. We demonstrate a semantic search framework that achieves a comprehensive semantic understanding of the entire notebook's contents, enabling it to effectively handle various types of user queries. Key components of this framework include: 1). A data preprocessor is designed to handle diverse types of cells within Jupyter Notebooks, encompassing both markdown and code cells. 2). An innovative methodology is devised to address token size limitations that arise with code-type cells. We implement a finer-grained approach to data input, transitioning from the cell level to the function level, effectively resolving these issues.

cs.IR↗