A new method using Ising models improves LLM judge aggregation by accounting for correlations between judges rather than assuming independence. When multiple language model judges evaluate the same item, their agreement may appear stronger than warranted if they share training lineages or prompts. The proposed approach models judges as a network, learning both individual reliability and pairwise dependencies, outperforming traditional weighted voting by 9-14% across three tasks.