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
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.
Method46 connections
Related3
Nearby in the graph rather than deliberately chosen. Looser, sometimes surprising.
Linked from7
- Automation BiasMethod
Over-trusting an automated recommendation, including against available contrary evidence.
- 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.
- Segmented SleepBody & Medicine
The claim that pre-industrial night sleep came in two blocks separated by a waking interval, and the dispute over how general that was.
- SubagentAgents & Language Models
A model instance spawned by another to handle a scoped task with its own context.