In plain English
This page explains how AI systems can change over time through updates, tests, retraining, memory, and approvals even when no single model rewrites itself.
- Why this matters: AI risk can come from the whole arrangement, not one obvious model.
- What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
- Technical version below: the expert terminology remains available and is linked through the glossary.
What Would Count as Behavioral Extinction?
Behavioral extinctionEvidence that a behavior is no longer expressible across active artifacts, descendants, memory, routes, compositions, and retained training material. Deleting one model is not sufficient evidence. Open glossary definition requires evidence across active artifacts, descendants, memory, routes, compositions, and retained training material.
Mechanism
Variation, evaluation, selection, inheritance, and succession can exist as properties of the broader development process. The model does not need to rewrite itself at runtime. The ecology changes because operators, pipelines, routers, and release controllers alter the population.
Assurance implication
A descendant needs fresh evidence for safety-relevant behavior. A content hash can identify an artifact, but it cannot prove that a related descendant preserved all relevant guardrails.
Review question
What behavior is being tracked, where could it be encoded, which descendants or reservoirs may carry it, and what evidence would count as absence across active compositions?
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Extinction is an evidence claim
Behavioral extinction means there is evidence that a behavior is no longer expressible across active artifacts, descendants, memory, routes, compositions, retained training material, and evaluatorA system that judges whether an AI output or candidate is acceptable. Open glossary definition preferences within a defined scope. It is stronger than deletion. It is also never absolute outside the reviewed boundary.
What must be checked
The review should define the behavior precisely, list current and historical carriers, identify descendants, inspect synthetic data and retained outputs, examine memory snapshots, test active router paths, review adapters and merge products, and replay probes under current evaluator versions. It should also state which systems were not checked.
What weak evidence looks like
Weak evidence includes “the old model was deleted,” “we could not reproduce it once,” “the average benchmark improved,” or “the new model card says the issue is fixed.” These may be relevant facts, but they do not establish extinction across an ecology.
Operational result
A behavioral-extinction review should end with one of three outcomes: extinction supported within scope, persistence path remains, or insufficient evidence. The third outcome is often the honest one.