🤖 AI Summary
This work addresses the limitations of traditional homogeneous parallel search, which is constrained by the inductive bias of a single language model and struggles to generate behavioral novelty. The authors propose the DEI framework, which for the first time employs heterogeneous large language models as mutation operators within distributed evolutionary nodes. By leveraging non-blocking collective communication to share local optima, DEI establishes a cross-model adversarial-cooperative mechanism that enhances both diversity and robustness. Empirical results demonstrate that model heterogeneity—not merely parallel scale—is the key driver of improved performance in language model–based quality-diversity (LLM-QD) optimization. On the Core War benchmark, a four-node heterogeneous system achieves a 124% increase in QD-Score (45.90 vs. 20.46) and a 28% improvement in coverage (80.6% vs. 63.0%) over the single-node baseline, consistently outperforming homogeneous parallel alternatives.
📝 Abstract
We present DEI: Diversity in Evolutionary Inference, a distributed Quality-Diversity (QD) search framework that assigns heterogeneous large language models (LLMs) as mutation operators across peer nodes communicating with non-blocking collective operations. Unlike homogeneous parallel search, which replicates a single model's inductive biases across all workers, DEI treats each LLM's distinct creative prior as a complementary source of behavioral novelty. Extending the Digital Red Queen framework with DEI, nodes share local optimal solutions at the end of each round to seed the next round's population. This creates cross-model adversarial 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 inside a simulated machine, a four-node heterogeneous ensemble (GPT-5.4-mini, Claude Sonnet 4.6, GPT-5.2, and Claude Haiku 4.5) achieves 124 percent higher merged-archive QD-Score (45.90 vs. 20.46) and 28 percent higher coverage (80.6 percent vs. 63.0 percent of cells) than a single-node baseline at equal total LLM-call budget. The heterogeneous 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.