Abstract
A policy-governed Large Language Model (LLM) gateway is provided that cuts token spending on large, mixed-context requests by virtualizing high-cost prompt segments into secure pointers and rehydrating them only when needed via controlled tool retrieval. The system is designed to preserve output quality and operational safety through route-aware fail-open controls rather than lossy compression or blind truncation. The result is auditable, per-request cost reduction that supports enterprise-scale artificial intelligence (AI) adoption without requiring application workflow changes.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
C Kuehnl, Todd, "POLICY-GOVERNED LLM CONTEXT VIRTUALIZATION WITH TOOL-MEDIATED REHYDRATION FOR AUDITABLE COST REDUCTION", Technical Disclosure Commons, (June 25, 2026)
https://www.tdcommons.org/dpubs_series/10565