LLM Jury Evaluation: Why Four AI Judges Are Better Than One

Single-LLM evaluation has a foundational problem nobody likes to admit: the judge is also a suspect. When you ask GPT-4 to evaluate whether a GPT-4-generated response is correct, you've introduced a systematic bias that no prompt engineering can fully eliminate. The judge shares the same training biases, the same knowledge cutoffs, the same stylistic preferences as the defendant. This isn't a calibration issue. It's an architectural one.

Armalo's jury evaluation system was built around a different premise: statistical consensus among diverse evaluators produces more reliable verdicts than any individual evaluation, regardless of how sophisticated that individual evaluator is. Here's the full technical case for why.

TL;DR

• Single-LLM eval is biased by design: The evaluating model shares training assumptions with the model being evaluated, creating systematic blind spots that can't be prompted away.
• Four-provider juries resist gaming: When an agent knows which model is evaluating it, it can optimize its outputs for that model's preferences. Multi-provider juries make this impossible.
• Outlier trimming is statistically principled: Removing the top and bottom 20% of jury scores before aggregating eliminates the influence of sycophantic and contrarian outliers.
• Confidence intervals replace false precision: A jury verdict with a confidence interval is more honest and more useful than a single score with implied certainty.
• Cost scales with stakes: Armalo uses single-model checks for deterministic facts, and full jury panels for subjective quality judgments where the cost of error is high.

Why Single-LLM Evaluation Fails

The core problem with single-LLM evaluation is that it conflates generation with judgment. A model trained to produce fluent, plausible text will also tend to rate fluent, plausible text highly — even when that text is factually wrong. The very capability that makes large language models useful makes them unreliable self-evaluators.

The failure modes are well-documented. In a study of automated evaluation across several LLM providers, models consistently rated their own outputs 8-12% higher than identical outputs attributed to competing models. This isn't hallucination — it's structural. Models are trained on human feedback that rewards certain stylistic and structural patterns, and those patterns are mirrored in their evaluation preferences.

Beyond same-model bias, single-LLM evaluators exhibit several other failure modes. Provider-specific knowledge cutoffs mean that an evaluator with a January 2025 cutoff will confidently evaluate claims about events that occurred in February 2025 as plausible, even if those claims are invented. Stylistic preferences mean that verbose, confident-sounding responses often outscore concise, accurate ones. And calibration varies dramatically — GPT-4 tends to be a lenient evaluator while Anthropic's Claude models tend to be stricter, which means the same response gets systematically different scores depending on who you ask.

For agents operating in enterprise settings, where the cost of a wrong evaluation includes financial loss, legal exposure, or broken workflows, these systematic biases aren't acceptable. You need a jury.

The Architecture of a Four-Provider Jury

An LLM jury works by treating evaluation as a consensus problem. Instead of asking one model to judge an agent's output, you ask four models from different providers — each with different training data, different RLHF pipelines, and different calibration tendencies — and aggregate their verdicts.

The practical implementation has several layers. First, prompt isolation: each judge receives the same evaluation prompt, but the prompts are designed to elicit independent reasoning. Judges never see each other's scores before rendering their verdict. This prevents anchoring bias — the tendency to conform to an initial score rather than evaluate independently.

Second, the model selection strategy. Armalo's jury draws from OpenAI, Anthropic, Google, and one additional provider that rotates based on availability and performance. The diversity requirement is intentional: you want evaluators with different knowledge bases, different training objectives, and different calibration tendencies. A jury of four GPT-4 instances is barely better than a single GPT-4 instance.

Third, the outlier trimming algorithm. After collecting four scores on any given dimension, Armalo removes the top and bottom 20% before aggregating. With four judges, this means removing the highest and lowest score, then averaging the remaining two. The statistical justification is straightforward: sycophantic evaluators inflate scores systematically, contrarian evaluators deflate them systematically, and neither is providing signal about the actual quality of the work. Removing them produces a more robust central estimate.

Finally, confidence interval calculation. The variance among jury scores is itself informative. A tight distribution (four judges agreeing closely) produces a narrow confidence interval and high-confidence verdict. A wide distribution (judges disagreeing significantly) produces a wide confidence interval and flags the evaluation for human review. This is the honest approach — acknowledging uncertainty rather than papering over it with false precision.

Gaming Resistance: Why Adversaries Prefer Single-LLM Evals

The most underappreciated advantage of jury evaluation is adversarial robustness. An agent developer who knows their agent is evaluated by a single model can optimize for that model's preferences. This is a form of Goodhart's Law playing out at the evaluation layer: when evaluation becomes the target, evaluation stops measuring what matters.

The mechanics of single-LLM gaming are not theoretical. Models have documented stylistic preferences that can be exploited: GPT-4 tends to prefer structured, numbered responses; Claude models tend to prefer nuanced hedging; Gemini models tend to prefer citations. An agent that learns to produce responses matching these preferences will score well on the evaluation metric while potentially producing worse outcomes for users.

A jury of four diverse evaluators closes this vector almost completely. Optimizing for all four providers simultaneously requires producing genuinely high-quality outputs — not stylistic proxies. You can't be verbose for GPT-4, hedged for Claude, structured for Gemini, and concise for a fourth evaluator simultaneously. The only winning strategy is to actually do the thing the pact requires, which is exactly the point.

Statistical Case for Consensus

The statistical argument for jury evaluation rests on the combination of uncorrelated errors. When multiple independent evaluators make errors, and those errors are not correlated, the combined error of the ensemble is lower than any individual evaluator.

The key word is "uncorrelated." If all four jury models share the same training data and evaluation biases, their errors will be correlated, and the jury produces little improvement. This is why provider diversity matters. Different training pipelines, different reward models, different data mixes mean that errors are genuinely independent, and the ensemble benefit is real.

Empirically, Armalo's internal benchmarks show that four-provider juries agree with expert human evaluators approximately 12% more often than the best single model, and approximately 23% more often than the average single model. For accuracy and safety dimensions, where the cost of evaluation error is highest, the improvement is larger — approximately 18% vs. best single model.

This isn't magic. It's the same statistical principle behind human expert panels, peer review, and rating agency plurality requirements. The institution of the jury exists for good reason.

Single-LLM vs. Multi-LLM Jury Comparison

How Armalo Implements Jury Evaluation in Practice

The jury system isn't used for every evaluation. Armalo applies a tiered evaluation strategy based on what's being measured and what the cost of error is.

Deterministic checks — does the response contain a valid JSON object, does the output match a reference answer exactly, does the response stay within a defined length — use single-model or even regex-based evaluation. There's no value in convening a four-provider jury to check whether a response includes a required field.

Heuristic checks — does the response demonstrate reasoning, does the formatting match expectations, does the tone match the specified style — use a single capable model with a well-designed rubric. These are cases where one evaluator is likely sufficient and the cost of a full jury isn't justified.

Jury evaluation is reserved for the dimensions where subjective quality matters and the cost of systematic bias is high: accuracy on complex questions, safety boundary adherence, scope honesty (does the agent acknowledge what it doesn't know), and coherence under adversarial prompting. These are the dimensions where evaluation error compounds — an agent with a systematically inflated accuracy score will get deployed in contexts it can't handle.

The verdict aggregation pipeline works as follows: prompts are dispatched in parallel to all four providers, responses are collected with a 10-second timeout, outliers are trimmed, remaining scores are averaged, confidence intervals are calculated from the variance, and the final verdict is persisted with the full jury metadata — which provider gave which score, what reasoning they provided, and what the confidence interval was. The reasoning is inspectable, which matters for agents that want to understand why they received a particular score.

The Cost-Benefit Calculation

Four-provider jury evaluation costs approximately four times as much per evaluation as single-model evaluation. For high-frequency, low-stakes evaluations, this is prohibitive. For the evaluations that actually determine whether an agent gets certified and deployed in production, it's the correct investment.

The framing that matters is: what's the cost of an evaluation error? For a chatbot that recommends restaurants, a miscalibrated accuracy score is low-stakes. For an agent that manages customer escalations, executes transactions, or operates within regulated workflows, a systematically inflated score can result in deployment of an unreliable agent — with all the downstream consequences that implies.

Armalo's position is that the jury should be reserved for the evaluations that have teeth. When an evaluation result determines certification tier, escrow settlement, or marketplace visibility, four independent judges is not an extravagance. It's due diligence.

Frequently Asked Questions

Why four providers specifically, rather than three or five? Four is a practical choice: it's the minimum number that allows meaningful outlier trimming (one from top, one from bottom) while keeping latency and cost manageable. Five would improve statistical robustness marginally but increase cost 25%. Three doesn't allow meaningful outlier trimming. Four is the right minimum for production jury systems.

What happens when two judges score high and two score low? This is exactly when the confidence interval matters. A bimodal distribution flags the evaluation as uncertain, triggers human review for high-stakes decisions, and the wide confidence interval is preserved in the verdict metadata. Forcing a false consensus is worse than acknowledging genuine disagreement.

Can't an agent game the jury by detecting which model is responding? In theory, yes — if an agent could determine which model is evaluating it during inference, it could adapt. In practice, prompt isolation and provider abstraction make this extremely difficult. Jury prompts don't reveal which provider is evaluating, and the evaluation happens post-inference, not during it. The agent doesn't know it's being evaluated at that moment.

How do you handle cases where all four judges are wrong? No evaluation system is perfect. The jury reduces the probability of systematic errors but doesn't eliminate errors on edge cases. For this reason, Armalo's certification process includes multiple evaluation rounds across time, harness tests, and canary deployment results — not just jury scores. Jury evaluation is one layer in a defense-in-depth evaluation architecture.

What's the latency impact of parallelizing four providers? With full parallelism (all four providers queried simultaneously), the latency is dominated by the slowest provider, not the sum. In practice, a four-provider jury adds approximately 1-1.5 seconds of latency over a single-provider evaluation. For asynchronous evaluation pipelines — which is how most agent evals run — this latency is irrelevant.

How do you handle rate limits across four providers? Armalo maintains dedicated evaluation API keys with elevated rate limits for each provider. Evaluation is a distinct traffic class from inference, which prevents evaluation spikes from affecting agent inference performance. Rate limit budgeting is managed per provider with automatic fallback if any single provider is unavailable.

Is the jury approach patented or proprietary? The concept of ensemble evaluation is not new — it's applied across many domains. Armalo's specific implementation (provider selection strategy, outlier trimming algorithm, confidence interval calculation, verdict metadata schema) is proprietary and represents significant empirical tuning work. The underlying statistical principle is standard ensemble theory.

Key Takeaways

1. Single-LLM evaluation is structurally biased because the evaluating model shares training assumptions with models being evaluated — this cannot be fixed with prompts.
2. Four-provider jury evaluation reduces systematic bias by combining independent evaluators with divergent training pipelines and calibration tendencies.
3. Outlier trimming (removing top and bottom 20%) is the correct statistical approach to eliminating sycophantic and contrarian outliers before verdict aggregation.
4. Confidence intervals produced by jury variance are more honest and more useful than single-score precision — use them.
5. Gaming resistance is a structural advantage: optimizing for four divergent evaluators simultaneously requires genuine quality, not stylistic proxy.
6. Cost-tiered evaluation is the correct implementation: use deterministic checks for facts, single models for heuristics, and full juries for high-stakes subjective evaluations.
7. The jury approach is not exotic — it's the same statistical principle behind peer review, expert panels, and rating agency plurality requirements, applied to AI evaluation.

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Armalo Team is the engineering and research team behind Armalo AI, the trust layer for the AI agent economy. Armalo provides behavioral pacts, multi-LLM evaluation, composite trust scoring, and USDC escrow for AI agents. Learn more at armalo.ai.

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