Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases
- URL: http://arxiv.org/abs/2607.01425v2
- Date: Sat, 04 Jul 2026 12:11:09 GMT
- Title: Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases
- Abstract summary: Agent4cs is a framework that summarizes larges in a bottom-up fashion.<n>Agent4cs improves semantic consistency across all folder levels by average 8%.
- Score: 5.629159319634667
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding large, complex codebases, especially those with obfuscated structures and incomplete documentation, remains a significant challenge. Existing code summarization solutions often rely on a single language model or coding assistant like Claude Code, and treat source code as flat text, underutilizing the rich interdependencies and hierarchical information within a repository. To address these shortcomings, we propose Agent4cs - a multi-agent framework that summarizes large codebases in a bottom-up fashion, where a summarization agent focuses on producing robust summaries; a keyword-extraction agent proactively identifies critical information from subfolders; and a quality-assurance agent iteratively refines the outputs for readability, coherence, and completeness. Evaluated on 7 frontier models, Agent4cs improves semantic consistency across all folder levels by average 8% compared to two structured prompting baselines with code segments. Furthermore, extensive evaluation on real-world datasets demonstrates up to 38% gains in normalized keyword coverage rate over the same baselines.
Related papers
- Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation [74.0621258662676]
We propose Ptah, a multi-agent harness for interleaved report generation.<n>Ptah orchestrates the lifecycle from user query to rendered web report through planning, research, and writing stages.<n>A verifier agent serves as the harness's acceptance function, enforcing factual grounding, citation fidelity, and cross-modal consistency throughout the workflow.
arXiv Detail & Related papers (2026-05-28T12:40:34Z) - SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks [55.76734816061826]
We introduce SlopCodeBench, a language-agnostic benchmark comprising 20 problems and 93 checkpoints.<n>We track two trajectory-level quality signals: verbosity, the fraction of redundant or duplicated code, and structural erosion.<n>No agent solves any problem end-to-end across 11 models; the highest checkpoint solve rate is 17.2%.
arXiv Detail & Related papers (2026-03-25T19:26:44Z) - What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction [57.86097956633207]
method is a graph-based agent framework for generating executable code from academic papers.<n>On an extended ReproduceBench spanning 3 domains, 10 tasks, and 40 recent papers, method achieves an average performance gap of 10.04% against official implementations.
arXiv Detail & Related papers (2026-03-02T12:33:31Z) - Completion by Comprehension: Guiding Code Generation with Multi-Granularity Understanding [37.78627994991325]
CoCo is a novel framework that enables code Completion by of multi-granularity context from large-scale code repositories.<n>Experiments on CrossCodeEval and RepoEval benchmarks demonstrate that CoCo consistently surpasses state-of-the-art baselines.
arXiv Detail & Related papers (2025-12-04T07:37:59Z) - Progressive Code Integration for Abstractive Bug Report Summarization [0.32004175453646166]
We propose a progressive code-integration framework for abstractive bug report summarization.<n>Our approach incrementally incorporates long code snippets alongside textual content.<n>Our pipeline outperforms extractive baselines by 7.5%-58.2% and achieves performance comparable to state-of-the-art abstractive methods.
arXiv Detail & Related papers (2025-11-29T05:35:36Z) - CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases [7.75137961900221]
We present bftextCodeWiki, a unified framework for automated repository-level documentation across seven programming languages.<n>CodeWiki introduces three key innovations: (i) hierarchical decomposition that preserves architectural context across multiple levels of granularity, (ii) recursive multi-agent processing with dynamic task delegation for scalable generation, and (iii) multi-modal synthesis that integrates textual descriptions with visual artifacts such as architecture diagrams and data-flow representations.<n>CodeWiki achieves a 68.79% quality score with proprietary models, outperforming the closed-source DeepWiki baseline (64.06%) by 4.73%
arXiv Detail & Related papers (2025-10-28T13:52:46Z) - Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
arXiv Detail & Related papers (2025-09-25T14:05:55Z) - MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection [84.75972919995398]
This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles.<n>The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent, and (iv) a web-scraped data analyzer.<n>Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches.
arXiv Detail & Related papers (2025-08-13T19:14:48Z) - Code-Craft: Hierarchical Graph-Based Code Summarization for Enhanced Context Retrieval [0.0]
We present Hierarchical Code Graph Summarization (HCGS), a novel approach that constructs a multi-layered representation of a by generating structured summaries in a bottom-up fashion from a code graph.<n>HCGS consistently outperforms traditional code-only retrieval across all metrics.
arXiv Detail & Related papers (2025-04-11T20:57:27Z) - Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs [1.4932549821542682]
Existing methods primarily focus on smaller code units, such as functions, and struggle with larger code artifacts like files and packages.<n>This paper proposes a two-step hierarchical approach for repository-level code summarization, tailored to business applications.
arXiv Detail & Related papers (2025-01-14T05:48:27Z) - EpiCoder: Encompassing Diversity and Complexity in Code Generation [66.43738008739555]
Existing methods for code generation use code snippets as seed data.<n>We introduce a novel feature tree-based synthesis framework, which revolves around hierarchical code features.<n>Our framework provides precise control over the complexity of the generated code, enabling functionalities that range from function-level operations to multi-file scenarios.
arXiv Detail & Related papers (2025-01-08T18:58:15Z) - Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration [64.19431011897515]
This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution.<n>Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy.<n>In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9% of in-house issues faced by development engineers, and solved 43.3% of problems after manual intervention.
arXiv Detail & Related papers (2024-06-03T15:20:06Z) - A Holistically-Guided Decoder for Deep Representation Learning with
Applications to Semantic Segmentation and Object Detection [74.88284082187462]
One common strategy is to adopt dilated convolutions in the backbone networks to extract high-resolution feature maps.
We propose one novel holistically-guided decoder which is introduced to obtain the high-resolution semantic-rich feature maps.
arXiv Detail & Related papers (2020-12-18T10:51:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.