Adversarial Review
Reviewing work with an explicit mandate to refute it, from a perspective that did not produce it.
Adversarial review is critique with an assignment to disprove rather than to approve. The framing matters because a reviewer asked "does this look right?" is disposed to agree, while a reviewer asked "find where this is wrong, default to rejecting if unsure" applies real pressure.
Two structural requirements make it work.
The reviewer must be cold. A critic that inherits the author's context inherits the author's assumptions and blind spots. Self-review reliably declares work satisfactory and misses the things that most needed catching — dead exports, inconsistent naming, an assertion that never runs. Where a system delegates implementation, review has to come from a fresh perspective rather than from the implementer's own continuation.
Independent verdicts must be aggregated, not chained. Several critics reviewing the same finding through different lenses — correctness, security, does-it-actually-reproduce — catch failure modes that three identical passes cannot. Requiring a majority to survive filters plausible-but-wrong findings, which is the dominant failure mode of automated review.
The same discipline applies to humans reviewing machine output, and to the reviewer's own reasoning: a lint rule set to error plus a clean run feels like execution, but it is configuration plus an inference across a gap nobody tested. See Plausible Mechanism and Code Review.
See also4
Hand-picked in the note itself — the neighbours worth reading next.
LLM-as-Judge
Using a language model to score another model's output against a rubric.
Agents & Language Models10 connections
Subagent
A model instance spawned by another to handle a scoped task with its own context.
Agents & Language Models11 connections
Multi-Agent Orchestration
Coordinating several model instances on one body of work, each with its own context.
Agents & Language Models10 connections
Falsifiability
A claim is only worth something if you know what observation would refute it.
Method16 connections
Related3
Nearby in the graph rather than deliberately chosen. Looser, sometimes surprising.
Linked from5
Notes elsewhere in the wiki that reach for this one.
- Code ReviewVersion Control & Delivery
A second person reading a change before it lands, and the practices that make it worth the time.
- LLM-as-JudgeAgents & Language Models
Using a language model to score another model's output against a rubric.
- Multi-Agent OrchestrationAgents & Language Models
Coordinating several model instances on one body of work, each with its own context.
- Plausible MechanismMethod
A causal explanation that was inferred rather than tested, and reads as more rigorous for being specific.
- SubagentAgents & Language Models
A model instance spawned by another to handle a scoped task with its own context.