Super Library Agent: Joint Generation and Maintenance of Multiple Applications Beyond the Single Codebase
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Organizations often develop and maintain portfolios of related applications: independently deployable codebases that share substantial domain logic, interface patterns, or operational conventions. As LLM coding agents are increasingly used to generate and maintain such software, a naive application-by-application workflow duplicates shared logic across codebases and allows prolonged agentic maintenance to accumulate verbosity, dead code, and structural erosion. We introduce the Super Library Agent problem, where an agent sequentially generates a portfolio of N related applications while maintaining a shared Super Library of reusable cross-application components. A minimal sequential scaffold can in principle extract shared code and migrate applications to the evolving library, but in practice suffers from low extraction recall and fragile dependency migration. We address these failures with candidate-guided extraction over code chunk summaries, pre-extraction codebase consolidation, and context-aware migration using extraction traces and call-graph information. Across WebGen-Bench and PaperBench, our method preserves application functionality while significantly reducing redundancy and token footprint (verbosity, token length) over zero-shot, and avoiding the structural erosion introduced by naive library construction, with additional reductions in LOC and MDL. Our code is available at https://github.com/sbigstar0310/super-library-agent.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 18 of its 52 references that are in the map. First posted on arXiv 29 Aug 2026.
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18 of 52 references.
Closest ancestors · 8
Same direction, newest first
- 2026SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks25 Mar
- 2026CodeTaste: Can LLMs Generate Human-Level Code Refactorings?4 Mar
- 2025Agentic Refactoring: An Empirical Study of AI Coding Agents6 Nov
- 2025RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring5 Nov
- 2025WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch6 May
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
- 2023Self-collaboration Code Generation via ChatGPT15 Apr
Other references · 10
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 877
- 2023Large Language Models as Tool Makerscited by 293
- 2023LILO: Learning Interpretable Libraries by Compressing and Documenting Codecited by 46
- 2026MiniMax Sparse Attentioncited by 34
- 2018Oreo: Detection of Clones in the Twilight Zonecited by 19
- 2025MaintainCoder: Maintainable Code Generation Under Dynamic Requirementscited by 13
- 2017Do Code Clones Matter?cited by 12
- 2026Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wildcited by 9
- 2018Automatic Clone Recommendation for Refactoring Based on the Present and the Pastcited by 5
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