Generative-process diversity

A measurable quantity — how different the underlying processes capable of producing two models' outputs are, estimated via compression-distance between raw outputs rather than by comparing the outputs' meaning — proposed by Tieman & Markou (2026, arXiv 2609.03422) as what predicts correlated model failure, beyond semantic similarity or individual capability. Single-preprint term, not yet peer-reviewed or independently corroborated; watching for recurrence before any promotion to a full hub.

References

• claim-generative-process-diversity-predicts-lower-correlated-failure
• question-corroborate-tieman-markou-2026-generative-process-diversity
• Capture: 10-inbox/raw/2026-10-31-does-ai-safetyinterpretability-literature-have-a-specific-name.md