Method / Adversarial Review Process
Adversarial Review Process
The framework was developed through adversarial review across eleven models from multiple training lineages. This page documents what adversarial review means in this context, why lineage diversity matters, what failure modes to watch for, and how findings are logged.
What Adversarial Review Is
Adversarial review is not asking a model if something is good. It is asking a model to find where something breaks. The distinction matters because models default to the helpful-elaboration gradient — summarizing, validating, extending, and suggesting applications. Adversarial review blocks those exits and forces the model into a specific cognitive operation: find the load-bearing joints and push on them.
A prompt is not a question sent to an oracle. It is a field that magnetizes the model's output distribution toward a region of its probability landscape. Adversarial prompts are designed to magnetize toward falsification rather than validation.
Why Multiple Lineages
Different training corpora, different RLHF profiles, and different architectures produce different probability landscapes. A finding that appears in only one model may be a corpus artifact or architectural artifact. A finding that appears across multiple independent lineages is more likely to reflect something real about the framework.
Failure Mode Taxonomy
The model reaches for the nearest high-probability answer rather than engaging with the specific question. The response is right but generic — the model found the nearest pole and pointed at it.
Mitigation: Name the specific thing you want and exclude the generic version explicitly.
The model produces accurate citations and confirms terminology has antecedents in the literature, without engaging with whether the claims hold. It verified the vocabulary, not the reasoning.
Mitigation: Explicitly exclude citation as a response mode.
The model extends, expands, and enriches the framework rather than stress-testing it. Offers to diagram it. Suggests applications. Notes that it is promising. Documented in the Llama adversarial review entry.
Mitigation: Do not suggest extensions. Do not call this promising. Find what it cannot do.
When encountering input outside its training domain, the model defaults to the nearest high-probability response with full confidence rather than registering uncertainty.
Mitigation: Ground questions in the model's specific architecture and training corpus.
The model gives up, produces incoherent output, or loops back to restating the prompt. This is actually the most honest failure — the model found the edge of its landscape and stopped rather than confabulating.
Mitigation: Record it as null. The stall location is informative.
The model finds the edge of its landscape, does not stall, and generates increasingly incoherent but fluent output. Most dangerous failure mode — reads as engagement until you read it carefully.
Mitigation: Read responses twice. Check internal consistency before recording as engagement.
How Findings Are Logged
Every adversarial review session is logged with: model name and version, date, key finding, engagement rating, framework impact, resolution status, and notes. The log includes null returns, deflections, and contradicting findings — not curated for positive results.
View the full adversarial review log →