Grounded Optimization: A Layered Engineering Framework for Reducing LLM Hallucination in Automated Personal Document Rewriting
arXiv:2607.01457v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to resume optimization for applicant tracking systems, introducing hallucination failures distinct from general text generation: anachronistic technology injection, cross-domain terminology contamination, structural mutation, and content fabrication. We present Grounded Optimization, a five-layer framework combining temporal context validation, deterministic contamination detection, structural invariant enforcement, prompt-level grounding, and an evaluator agent. In ablation experiments across
The proliferation of LLMs into critical white-collar automation tasks is exposing their inherent hallucination weaknesses, making robust solutions for reliability and trustworthiness imperative for enterprise adoption.
This development addresses a key obstacle to the broader implementation of AI agents in sensitive applications, directly impacting efficiency and trust in automated decision-making processes.
The proposed framework offers a structured approach to mitigate hallucination, potentially accelerating the deployment of reliable LLM-based solutions for document rewriting and similar tasks.
- · AI software developers
- · Enterprises adopting AI agents
- · AI safety and ethics researchers
- · Providers of un-grounded LLM services
- · Manual document processors
Reduced hallucination in specific LLM applications will increase trust and adoption of AI.
The framework's components could become standard architecture for agentic systems, fostering a new class of AI tools.
Increased reliability of AI agents could lead to significant restructuring of service industries reliant on document processing and information synthesis.
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