Inferred Generative-Process Diversity Predicts Correlated Failure Across Language Models

As language models are increasingly deployed in multi-model systems, robust system function depends on constituent models failing independently. Diversity is widely observed to buffer complex adaptive systems against correlated failure, but only when the type of diversity is relevant to the failure mode of concern. Existing assessments of model diversity often focus on semantic differences between outputs. We instead consider inferred generative-process diversity, the difference between inferred processes capable of generating observed outputs. We estimate this using Normalised Compression Distance between model output byte strings residualised against a permutation control. Estimated from responses of 38 language models to open-ended chat prompts, the measure predicts which model pairs choose the same wrong answer on ten disjoint benchmark families, beyond semantic similarity and capability, with a negative partial association on every benchmark (mean $-0.216$, 95% CI $[-0.309,-0.122]$). Semantic distance predicts no reduction in correlated failure once process diversity is held fixed. Five-model ensembles selected to maximise the Vendi score of the measure at fixed semantic distance converge on the same wrong answer less often than capability-matched random ensembles in every semantic band, whereas the reverse selection does not. Because it requires only observed outputs, inferred generative-process diversity provides a practical black-box approach for auditing and constructing model populations where correlated failure creates system-level risk.