DEI: Diversity in Evolutionary Inference\\ for Quality-Diversity Search

We present DEI: Diversity in Evolutionary In- ference, a distributed Quality-Diversity (QD) search framework that assigns heterogeneous large language models (LLMs) as mutation op- erators across peer nodes communicating with non-blocking collective operations. Unlike ho- mogeneous parallel search, which replicates a single model’s inductive biases across all work- ers, DEI treats each LLM’s distinct creative prior as a complementary source of behavioral nov- elty. Extending the Digital Red Queen frame- work with DEI, nodes share local optimal solu- tions at the end of each round to seed the next round’s population. This creates cross-model ad- versarial pressure that drives robustness beyond intra-model self-play. Evaluated on the Core War domain, a competitive programming benchmark in which Redcode warrior programs battle in- side a simulated machine, a four-node heteroge- neous ensemble (GPT-5.4-mini, Claude Sonnet 4.6, GPT-5.2, and Claude Haiku 4.5) achieves +124% higher merged-archive QD-Score (45.90 vs. 20.46) and +28% higher coverage (80.6% vs. 63.0% of cells) than a single-node baseline at equal total LLM-call budget. The hetero- geneous ensemble also outperforms an equally- budgeted homogeneous ensemble on QD-Score, coverage, and held-out solution generality across all four model families. These results provide the first empirical evidence that model diversity, not merely parallelism, is the key driver of gain in distributed LLM-based QD search.