Hierarchical Online Prompt Mutation with Dual-Loop Feedback for Guardrailed Evidence Document Generation: A Production-Evaluation Case Study
arXiv:2606.01472v1 Announce Type: cross Abstract: High-stakes production document-generation systems require language models to be adaptive, evidence-grounded, and auditable. We present HOPM, a hierarchical online prompt mutation framework evaluated on a real marketplace dispute-evidence workflow. HOPM treats prompts as online policies: a family/version router selects a prompt, deterministic guardrails attribute failures to mutable prompt-token categories, and dual feedback from human review and an automated judge updates both routing and mutation priorities. The primary evidence is an observe
The increasing deployment of large language models in high-stakes environments necessitates robust guardrails and adaptive systems capable of real-time learning and auditing.
This research outlines a practical framework for deploying adaptive and auditable AI in critical applications, addressing core concerns about reliability and control in real-world use cases.
The focus on 'Hierarchical Online Prompt Mutation' and 'Dual-Loop Feedback' signifies a move towards more dynamic, self-correcting, and explainable AI in production systems, crucial for trust and widespread adoption.
- · AI platform developers
- · Enterprises deploying AI
- · Users of AI-powered applications
- · Developers of static, brittle AI systems
- · Organizations relying on manual AI supervision
Increased reliability and trustworthiness of AI systems deployed in critical enterprise functions.
Reduced operational costs and liability for companies utilizing AI for sensitive tasks due to improved guardrails and auditability.
Acceleration of AI adoption in highly regulated industries as the technology becomes more robust and compliant.
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