AGORA: Reliability-Aware Aggregation of LLM Judges via Item-Response Theory

LLM-as-a-judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they implicitly assume that judges provide independent estimates of true quality. However, in practice, LLM judges exhibit correlated errors caused by shared latent confounders -- such as verbosity, stylistic preferences, or training artifacts -- causing standard aggregation rules like majority vote or averaging to provide little gain or even amplify systematic mistakes. To address this, we introduce CARE, a confounder-aware aggregation framework that explicitly models LLM judge scores as arising from both a latent true-quality signal and shared confounding factors. Rather than heuristically re-weighting judges, CARE separates quality from confounders without access to ground-truth labels. We provide theoretical guarantees for identifiability and finite-sample recovery under shared confounders, and we quantify the systematic bias incurred when aggregation models omit confounding latent factors. Across 12 public benchmarks spanning continuous scoring, binary classification, and pairwise preference settings, CARE improves aggregation accuracy, reducing error by up to 26.8%. Code is released in https://github.com/SprocketLab/CARE.

We present the Judge Reliability Harness, an open source library for constructing validation suites that test the reliability of LLM judges. As LLM based scoring is widely deployed in AI benchmarks, more tooling is needed to efficiently assess the reliability of these methods. Given a benchmark dataset and an LLM judge configuration, the harness generates reliability tests that evaluate both binary judgment accuracy and ordinal grading performance for free-response and agentic task formats. We evaluate four state-of-the-art judges across four benchmarks spanning safety, persuasion, misuse, and agentic behavior, and find meaningful variation in performance across models and perturbation types, highlighting opportunities to improve the robustness of LLM judges. No judge that we evaluated is uniformly reliable across benchmarks using our harness. For example, our preliminary experiments on judges revealed consistency issues as measured by accuracy in judging another LLM's ability to complete a task due to simple text formatting changes, paraphrasing, changes in verbosity, and flipping the ground truth label in LLM-produced responses. The code for this tool is available at: https://github.com/RANDCorporation/judge-reliability-harness

LLM-as-a-judge systems often aggregate multiple judges to improve evaluation reliability. However, judge agreement can reflect shared biases toward verbosity, formatting, or fluency rather than the intended evaluation target. Addressing these shared biases is challenging because observational judge outputs alone cannot distinguish agreement driven by target quality from agreement driven by nuisance attributes. We introduce VoirDire, an intervention-calibrated aggregation method for LLM-judge panels. VoirDire applies controlled interventions that vary specified surface attributes while aiming to preserve the evaluation target; it then uses paired changes in judge scores to identify nuisance-sensitive directions. It attenuates these directions and selects aggregation weights that retain variation across items while penalizing sensitivity to the interventions, making the final consensus less dependent on superficial features. We show theoretically that observational judge outputs alone cannot identify which shared direction is target-relevant, while paired interventions provide the contrast needed to identify nuisance-sensitive variation. We evaluate VoirDire on 12 datasets spanning continuous scoring, binary classification, and pairwise preference. Compared with the existing LLM-judge aggregation baselines, VoirDire reduces MAE by up to 26.8% on continuous-score tasks and improves accuracy by up to 5.3 percentage points on binary and preference tasks. These findings suggest that counterfactual interventions provide an additional means of improving the reliability and controllability of LLM-based evaluation beyond merely relying on observed judge agreement.