---
title: "Generative-process diversity"
type: "entity"
entity_kind: "term"
status: "watching"
canonical_name: "Generative-process diversity"
aliases: []
first_seen: "2026-10-31T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["correlated errors","algorithmic monoculture","representational similarity"]
seek_code_commit: "d40ee39"
---


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 Single-preprint term, not yet peer-reviewed or independently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