Think Fast, Talk Smart: Partitioning Deterministic and Neural Computation for Structured Health Text Generation
arXiv:2605.29652v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being used to generate health text from structured records such as wearable time series, biomarkers, vitals, and care-management logs. For recurring health outputs, fluency is not enough: systems must remain faithful to source data, ground explanatory claims in available evidence, follow stated policies, emit machine-readable outputs, and run cheaply enough for repeated use. We ask which responsibilities in structured health generation should be deterministic computation rather than runtime LLM prompt
The proliferation of LLMs in sensitive domains like healthcare necessitates robust methods for ensuring accuracy, safety, and cost-effectiveness, pushing for hybrid architectures.
This research addresses critical limitations of pure LLM approaches in health text generation, ensuring greater trustworthiness, efficiency, and policy adherence for institutional applications.
The focus shifts from solely relying on LLM fluency to a hybrid paradigm that strategically integrates deterministic computation for reliability and cost-efficiency in regulated sectors.
- · Healthcare AI developers
- · Patients (improved accuracy)
- · LLM application platforms
- · Regulatory bodies
- · Pure generative AI solutions in healthcare
- · LLM inference providers (if disaggregated workloads reduce overall LLM usage)
Increased adoption of hybrid AI systems for sensitive data generation across various industries beyond healthcare.
Development of new architectural patterns and tooling specifically designed for partitioning AI workloads between neural and symbolic components.
Potential for regulatory frameworks to mandate explainability and verifiability through deterministic components in AI-generated content.
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Read at arXiv cs.AI