Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimization
arXiv:2607.01972v1 Announce Type: new Abstract: Large language models (LLMs) are often asked to produce JSON conforming to a fixed schema, powering information extraction, tool calling, agentic planning, and knowledge-graph construction. Measuring how closely an output matches a gold reference is essential yet surprisingly hard: exact match is brittle, text similarity ignores structure, and an LLM judge is expensive, opaque, and non-deterministic. We address this with Object Aligner (OA), an open-source Python library that scores two JSON objects deterministically by recursively aligning their
The proliferation of LLMs requiring structured JSON outputs for various applications necessitates more robust and transparent evaluation metrics beyond brittle exact matching or expensive LLM-as-judge methods.
A standardized, deterministic metric for JSON schema similarity directly addresses a critical pain point in developing and optimizing LLM applications, enhancing reliability and reducing operational costs.
The ability to accurately and efficiently measure the structural and semantic fidelity of LLM-generated JSON outputs will accelerate agentic system development and improve the robustness of LLM-powered workflows.
- · LLM developers
- · AI platform providers
- · Enterprises deploying LLM agents
- · Open-source AI tooling
- · Ad-hoc LLM evaluation methods
- · LLM-as-a-judge services (for JSON schema)
- · Manual JSON validation processes
Object Aligner becomes a standard evaluation metric for LLM JSON output quality.
Improved reliability and safety unlock new, more complex applications for LLM agents, particularly in structured data environments.
Increased adoption of LLMs for sensitive information extraction and automation tasks as confidence in structured output quality grows.
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