Libra: Training the Environment for Agentic Information Retrieval

• URL: http://arxiv.org/abs/2607.00016v1
• Date: Tue, 26 May 2026 17:32:14 GMT
• Title: Libra: Training the Environment for Agentic Information Retrieval
• Abstract summary: We present Libra, a framework that introduces mutable "catalogs" into the repository.<n>We show that this environmental healing yields continual, logarithmic improvements in code localization accuracy.<n>We also demonstrate that a minimalist coding agent equipped with Libra-optimized catalogs outperforms state-of-the-art baselines.
• Score: 2.483104923585137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Information localization within massive repositories is a cornerstone of agentic LLM systems. While synthetic data-driven optimization has proven successful in training LLMs, little attention has been paid to optimizing the agent's working environment (the repository itself) in a data-driven manner. To bridge this gap, we present Libra, a self-evolving framework that introduces mutable "catalogs" (hierarchical Markdown files serving as navigable indices) into the repository. Libra runs an LLM-driven optimization loop where a Prompter generates synthetic queries, a frozen Solver attempts to resolve them by navigating the catalogs, and a Healer rewrites the catalogs in response to the Solver's localization failures. Evaluations across 12 SWE-bench Lite repositories demonstrate that this environmental healing yields continual, logarithmic improvements in code localization accuracy. Furthermore, these environmental improvements transfer zero-shot across different LLMs and problem sets. Although the focus of this paper is to study the general behavior of such a system, we also demonstrate that a minimalist coding agent equipped with Libra-optimized catalogs outperforms state-of-the-art baselines. Code is available at https://github.com/salesforce-misc/Libra and data at https://huggingface.co/datasets/Salesforce/Libra.

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