KnowCoder-V2: Deep Knowledge Analysis

2025-06-07

AI Summary

Existing deep analytical frameworks face three critical bottlenecks: fragmented knowledge organization, exclusive reliance on online execution—hindering shareability and extensibility—and insufficient support for complex knowledge computation. To address these, we propose KDR, the first framework featuring a decoupled two-stage architecture—“knowledge organization” followed by “knowledge computation”—that synergistically integrates offline knowledge graph preconstruction with online LLM-driven analytical execution. We further design KCII, a unified code-generation interface, enabling end-to-end mapping from domain modeling to executable analytical code. Extensive evaluation across six categories and over 30 datasets demonstrates that KDR significantly outperforms state-of-the-art frameworks: it autonomously generates well-structured, insight-rich analytical reports, substantially enhancing both the effectiveness and reproducibility of large language models in deep knowledge reasoning and actionable insight generation.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Deep knowledge analysis tasks always involve the systematic extraction and association of knowledge from large volumes of data, followed by logical reasoning to discover insights. However, to solve such complex tasks, existing deep research frameworks face three major challenges: 1) They lack systematic organization and management of knowledge; 2) They operate purely online, making it inefficient for tasks that rely on shared and large-scale knowledge; 3) They cannot perform complex knowledge computation, limiting their abilities to produce insightful analytical results. Motivated by these, in this paper, we propose a extbf{K}nowledgeable extbf{D}eep extbf{R}esearch ( extbf{KDR}) framework that empowers deep research with deep knowledge analysis capability. Specifically, it introduces an independent knowledge organization phase to preprocess large-scale, domain-relevant data into systematic knowledge offline. Based on this knowledge, it extends deep research with an additional kind of reasoning steps that perform complex knowledge computation in an online manner. To enhance the abilities of LLMs to solve knowledge analysis tasks in the above framework, we further introduce extbf{KCII}, an LLM that bridges knowledge organization and reasoning via unified code generation. For knowledge organization, it generates instantiation code for predefined classes, transforming data into knowledge objects. For knowledge computation, it generates analysis code and executes on the above knowledge objects to obtain deep analysis results. Experimental results on more than thirty datasets across six knowledge analysis tasks demonstrate the effectiveness of KCII. Moreover, when integrated into the KDR framework, KCII can generate high-quality reports with insightful analytical results compared to the mainstream deep research framework.

Problem

• Lack systematic knowledge organization and management
• Inefficient online operation for large-scale knowledge tasks
• Inability to perform complex knowledge computation

Innovation

• Introduces offline knowledge organization phase
• Extends deep research with complex computation
• Bridges knowledge and reasoning via code generation