DEI: Diversity in Evolutionary Inference for Quality-Diversity Search

Hugging Face Daily Papers Papers
quality-diversity evolutionary-inference large-language-models heterogeneous-llms distributed-search core-war mutation-operators

Summary

DEI introduces a distributed Quality-Diversity search framework using heterogeneous LLMs as mutation operators, showing that model diversity improves performance over homogeneous parallel approaches. Evaluated on the Core War domain, a four-node heterogeneous ensemble achieves significant gains in QD-Score and coverage.

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.
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Source: https://huggingface.co/papers/2605.27130

Abstract

A distributed Quality-Diversity search framework uses heterogeneous large language models as mutation operators to enhance evolutionary inference, demonstrating that model diversity improves performance over homogeneous parallel approaches.

We present DEI: Diversity inEvolutionary Inference, a distributedQuality-Diversity(QD) search framework that assignsheterogeneous large language models(LLMs) asmutation operatorsacrosspeer nodescommunicating withnon-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 theDigital Red Queen frameworkwith DEI, nodes sharelocal optimal solutionsat the end of each round to seed the next round’s population. This createscross-model adversarial pressurethat drives robustness beyond intra-model self-play. Evaluated on theCore Wardomain, a competitive programming benchmark in whichRedcode warrior programsbattle 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-archiveQD-Score(45.90 vs. 20.46) and 28 percent highercoverage(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 onQD-Score,coverage, andheld-out solution generalityacross 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.

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