As the complexity of AI-operated projects increases, so does the opportunity for error. Assumptions are made. Details are missed. Focus drifts. And so, when the AI stops, the work often isn't done. The human operator is then left with reviewing the work, requesting changes, and then reviewing the new output. Skilled operators have already adopted correction practices to mitigate this problem.
One powerful technique is to recruit a second AI from another lab to perform a quality check. OpenAI ships an official plugin for software development that drops Codex into Claude Code as a reviewer.
A short name for it doesn't exist. Practitioners who write about the pattern reach for their own multi-word phrases, each a little different. You can't ask a team to "do the thing where the other lab's model argues," and a practice without a shared short name is hard to put in a checklist, budget for, or audit.
So, I propose a name:
Cross-Lab Adversarial Review (CLAR) is the practice of putting a model from a different lab against the work as an adversarial reviewer. The rival needn't be smarter; it's differently trained, so its blind spots are less likely to match. That is the value — decorrelation beats intelligence: their agreement carries real information, and the disagreement they can't resolve marks the call that still needs a human.
That definition is the whole of the term: a reviewer from a different lab, told to argue. Everything downstream — the evidence rules, the escalation, the tool that runs it — is how the practice gets implemented, not what makes something CLAR.
Why "Cross-Lab" is the Key
Adversarial review — telling any reviewer to challenge the work rather than rubber-stamp it — is good practice with any model. CLAR is differentiated by using a different lab. You find anecdotal evidence for model differentiation when talking to avid AI engineers: someone might prefer Opus for writing, Gemini for planning, and Codex for coding — three different labs. The difference is also backed by research.
The premise — that shared blind spots are the risk, and that crossing labs reduces them — is grounded in two findings. Great Models Think Alike and this Undermines AI Oversight (arXiv:2502.04313) finds model errors are substantially shared, and more so among more-capable models — the reason self- and same-model review is a weaker control than it looks. Correlated Errors in Large Language Models (Kim et al., arXiv:2506.07962) measures the overlap directly: models agree on the same wrong answers well above chance, with the correlation lower — but far from zero — across labs. Neither paper tests an adversarial-review protocol; they establish the problem CLAR responds to, not its effectiveness. Cross-lab review buys a discount on shared blind spots, not an exemption — which is why agreement stays evidence, not proof.
The first paper puts a direction on it: model errors, in its own framing, are becoming more similar as models grow more capable. Which turns the cheapest check you have — a model reviewing itself, or its sibling — into a control that's losing value.
Three Rules Define My CLAR Implementation
The definition stops short of any tool on purpose; the practice should outlive whatever implements it, and which labs and which models make sense for CLAR will change fast. But without discipline, a cross-lab review decays into two models trading opinions — confident, articulate, unfalsifiable. My implementation, below, is opinionated about three rules.
Every finding carries anchored evidence. A location and an observation — a line number, a named standard, a reproduction. Never a vibe. Evidence is what lets the first model check the claim against the actual artifact instead of debating tone.
A rejection needs contradicting evidence too. Verification is symmetric. The first model saying "I disagree" doesn't dismiss a finding; if it can't produce proof against it, the finding survives — recorded as a deadlock rather than waved away. Agreeableness corrupts a review in both directions.
The escalation is the deliverable. Most findings resolve — verified and fixed, or refuted with proof. What reaches the operator is the short list neither model could settle. If everything escalates, you've built a forwarder. If nothing escalates, you've built a rubber stamp.
For example, while building this site, Claude and Codex deadlocked on
a diagram's color — Codex citing a WCAG contrast threshold for the mark
against its background, Claude citing the design system's canonical
diagram-ink token (specified in the website repository's
DESIGN.md file), both right about their own half. Neither
could win on the contradictory evidence, so the question reached me: the
one decision in that batch of work that was actually mine.
The dozen or so other unambiguous mistakes were identified and sorted by a referee this essay names later. Claude automatically fixed those errors before I ever saw them.
What CLAR Is Not
A new discovery. The human antecedent is adversarial collaboration — opponents designing a fair test together, with an arbiter — as practiced by Mellers, Hertwig, and Kahneman (2001); CLAR points the same posture at models. The AI lineage approaches it from other angles: AI safety via debate (Irving et al., 2018) has competing agents argue before a human judge; multiagent debate (Du et al., 2023) improves answers by argument among instances of one model; LLM-as-a-judge (Zheng et al., 2023) has an external model score outputs; and PoLL (Verga et al., 2024) already juries across providers, aimed at judge bias and cost. Practitioners have described the cross-vendor adversarial version under longer names — "cross-model adversarial review" (Vaughan), "adversarial cross-model review" (Wade). CLAR is a short label and a discipline for a practice that was already emerging. The term was introduced here in July 2026.
The Reference Implementation
That referee is Impasse, the reference implementation of CLAR — open source, MIT-licensed, and deliberately dull in its mechanics. It hands the artifact to a rival-lab model with instructions to refute, requires structured findings with anchored evidence, and routes only genuine deadlocks to a person. It reviews more than code: a decision memo, an essay, a research claim, a dataset. And it is read-only on the work — the reviewer criticizes; it never edits. One caveat worth checking: Impasse selects the reviewer by provider relative to your host, and some configurations can't confirm the pick is genuinely a different lab. If yours can't confirm it, you haven't run a CLAR pass — you've bought a second opinion from the same blind spots. Verify the provenance.
The field story — what a rival reviewer caught over a day of real work, what it got wrong, what the arguing looked like — is in AI's Second Opinion.
What It Costs
Running CLAR uses more tokens, and requires access to a second lab's model. Wiring one agent to call another reliably takes work. And the failure mode is quiet: let the two models share a prompt, a style guide, a set of assumptions, and you've collapsed them into one reviewer with two logos — you've paid to agree with yourself more confidently. While the cross-lab review provides the most independent viewpoint, the more of that shared framing you hand the reviewer, the more its verdict slides back toward the first model's mold.
Decorrelation is something you protect, not something you get free with the second login. A CLAR pass is only as good as the difference between the two models you feed it.
AI Workshop for CEOs
Deciding which of your team's decisions deserve a CLAR pass — and which disagreements should reach a human — is exactly the kind of operating discipline the workshop builds with your team. Three hours live with a group of 8 CEOs, plus a 1-on-1 to fit it to how your work actually gets done.
Reserve Your Seat →The day-by-day account of this practice in the field — the deadlocks, the catches, the costs — is in AI's Second Opinion, and a single catch worth its own write-up — a spam filter that passed its own tests but would have dropped real leads — is in Green Tests, Two Bugs. Why the harness around a model matters more than the model is in the harness beats the model; how operators structure whole agent fleets is in your AI agents need an org chart.