arXiv:2605.13076v2 Announce Type: replace Abstract: The LLM-based generation of machine-readable outputs such as JSON has attracted significant attention for integration with external systems. However, existing approaches cannot strictly enforce the maximum number of tokens to be generated, leading to infinite generation or truncated outputs that cause a system malfunction. To address this limitation, we propose TruncProof, a novel grammar-constrained generation method that enables LLMs to produce grammatically valid JSONs while adhering to a predefined token limit. By leveraging the propertie
The proliferation of LLM-based systems integrating with external APIs and databases necessitates robust and reliable machine-readable output generation under practical constraints. This research addresses a critical technical hurdle in making LLMs more production-ready for such tasks.
Reliable and predictable JSON generation from LLMs under token constraints is fundamental for building stable and scalable AI-driven applications and reducing integration friction with existing software infrastructure.
LLMs can now generate structurally valid JSON within strict token limits, mitigating common failure modes like infinite generation or truncated, unparseable output, thereby enhancing the reliability of AI agents interacting with systems.
- · AI developers
- · Automated API integration platforms
- · LLM-as-a-service providers
- · Software engineers
- · Systems prone to parsing invalid JSON
- · Inefficient LLM output post-processing solutions
This enables more robust and predictable integration of LLMs into critical enterprise and consumer applications.
The increased reliability of LLM-generated structured data accelerates the development and deployment of complex AI agents and workflows.
This could lead to a broader adoption of LLMs for tasks requiring precise, machine-readable output, impacting how software is designed and maintained.
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Read at arXiv cs.CL