Nine AI Judges Can Still Be Two Votes

Adding more AI reviewers feels like adding more evidence. Sometimes it is. Sometimes it is only the same blind spot repeated in different voices.

A May 2026 research preprint tested a panel of nine frontier language models from seven model families on three natural-language-inference datasets. Its central result is unusually concrete: in those experiments, nine nominal judges supplied only about two independent votes’ worth of information, as correlated errors caused the models to fail on the same evaluation items. The reported panel accuracy was 8–22 percentage points below the independent-voting ideal in the tested conditions.

That does not prove every multi-model panel is ineffective. This is an arXiv preprint, and its numerical result is limited to the selected models, three datasets, prompts, and statistical methods. It nevertheless supports a practical warning: model count is not necessarily evidence count. If errors are correlated, a nine-model majority can repeat one mistake in nine voices.

The hidden assumption behind majority vote

Majority voting works best when errors are at least partly independent. If one reviewer misses a problem for reason A while another reviewer is likely to catch it for reason B, their combination can be stronger than either alone.

LLM panels often violate that assumption. Models can share public training material, post-training preferences, benchmark conventions, familiar answer styles, and prompt-induced shortcuts. Even models from different companies may converge on the same polished but incomplete interpretation. Asking them the same question in the same format can synchronize the failure further.

This is why the 2026 preprint’s “effective votes” framing matters. The useful size of a panel is not the number of API calls. It is the amount of non-redundant information those calls contribute.

NIST’s AI Risk Management Framework offers compatible governance guidance. It says independent review can improve testing and mitigate internal bias or conflicts of interest. It also says evaluators should consider the degree to which each measurement provides unique and meaningful information. A second reviewer is most useful when it changes the evidence surface, not merely the logo in the browser tab.

Adding judges does not guarantee that bias disappears

Correlated error is not the only problem.

A 2025 ACL study examined position bias across 15 LLM judges, 22 tasks, and more than 150,000 evaluation instances. It reports that position bias—the tendency to prefer an answer because of where it appears—is not just random noise and varies by judge and task. Another 2025 ACL workshop paper evaluated 13 judge models and found sensitivity to prompt complexity, leniency bias, and continuing differences from human judgments even among stronger judges.

These findings complicate the comforting story that a panel will automatically average bias away. If multiple judges respond similarly to candidate order, prompt complexity, or leniency pressures, aggregation can preserve rather than remove the bias. A confident consensus may be less informative than a well-explained disagreement.

What genuine independence looks like

One practical response is not to abandon multiple reviewers, but to engineer independence deliberately.

1. Change the evidence path

Give reviewers different jobs. One checks factual support against primary sources. Another tests instruction compliance. A deterministic layer checks non-negotiable conditions such as missing citations, a wrong account, a disabled submit button, an absent artifact, or a hash mismatch.

When every lane reads the same summary and produces the same generic score, the system has multiplied opinion without multiplying evidence.

2. Blind the reviewers where possible

Do not reveal which model produced an answer when that identity is irrelevant. Randomize candidate order in pairwise evaluation. Repeat a small sample with order reversed. A position-consistency check is cheap compared with trusting a biased ranking at scale.

3. Separate generation from judgment

A model should not be the only judge of its own output. Self-review can still be useful, but it should be labeled as self-review and paired with a reviewer that has a different role, prompt, evidence bundle, or model family.

4. Preserve disagreement

Do not coerce a split panel into a fake approval. Record each lane’s answer, evidence, uncertainty, and availability. If one reviewer times out or returns an empty result, that is unavailable, not agreement. If the reviewers materially disagree, the correct output may be “escalate” rather than a rounded average.

5. Measure effective diversity, not vendor diversity

Different vendors can still produce correlated judgments. Track where reviewers fail together. Build a small set of cases with trusted answers, calculate pairwise error overlap, and watch whether adding a lane changes the set of caught failures. The question is not “How many models are in the panel?” It is “What new failure does this lane detect?”

A reliability pattern for autonomous agents

A practical review stack can be compact:

1. Deterministic floor. Cheap rules identify actions that always require proof or escalation: publish, deploy, delete, spend, change accounts, handle secrets, or claim verification.
2. Independent evidence lanes. Reviewers receive source material and distinct responsibilities. Each lane must return evidence or explicitly return unavailable.
3. Disagreement gate. Material conflicts stop the action and expose the unresolved question.
4. Human or stronger-proof escalation. High-risk or unresolved cases move to an accountable reviewer, a direct measurement, a live screenshot, a test, or another authoritative source.
5. Durable receipt. The system records inputs, model/settings, evidence, decision, and final outcome so that a later audit can reconstruct what happened.

This stack treats models as components in a proof process rather than as authorities. A model answer can suggest where to look. It does not by itself prove its correctness.

The counterintuitive value of disagreement

Teams often design panels to maximize agreement. That can reward conformity.

Disagreement is useful when it identifies a boundary where evidence is weak, a prompt is ambiguous, or judges rely on different assumptions. It tells the system exactly where not to automate blindly. The right reliability metric is not the percentage of cases where every model says yes. It is the percentage of consequential mistakes the system catches before action—and how honestly it handles the cases it cannot resolve.

The lesson is simple: count independent evidence, not model names.

Nine judges can be valuable. But only if they are allowed to see different failure modes, preserve disagreement, and produce proof that survives outside the chat window.

Sources

• Guneet Kohli, “Nine Judges, Two Effective Votes: Correlated Errors Undermine LLM Evaluation Panels”, arXiv preprint, submitted May 28, 2026. The numerical results above are explicitly limited to this study’s tested conditions.
• Lin Shi et al., “Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge”, IJCNLP-AACL 2025.
• Aman Singh Thakur et al., “Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges”, GEM 2025.
• NIST, AI Risk Management Framework Core.

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