Positional Restructuring of System Prompts: Mitigating Transformer Attention Bias to Enable Ground Truth Compliance in Sub-Frontier Language Models

This paper presents evidence that the primary barrier to reliable fact recall in sub-frontier language models deployed with system prompts is positional attention bias — the lost-in-the-middle phenomenon. In a systematic evaluation across five models and two model families, we discovered that restructuring the system prompt to place critical facts at the beginning and end raises a 14-billion parameter model's score from 5.7/10 to 7.5/10 on a seven-dimension verification battery, without any modification to model weights. The paper further describes Ground Truth Engineering, a systematic behavioural rule methodology that raised a 32B model from 6.2/10 to 9.4/10 with cold-restart persistence, and an automatic correction persistence pipeline. These findings enable sovereign AI deployment on consumer hardware costing under $500.