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As LLMs are increasingly deployed as automated evaluators ("judges") to replace costly human evaluation, researchers are documenting systematic scoring biases and developing correction methods, testing frameworks, and cheaper alternatives — a topic with direct relevance to AI governance, benchmarking integrity, and legal/compliance evaluation pipelines. PRISM (Probe-based Residual Inference of Scoring Misalignment) holds evaluated content fixed and measures how a judge's score shifts when specific attributes are explicitly emphasized or de-emphasized, using these counterfactual probes to model and correct implicit scoring bias (arXiv:2610.18389, Oct 14). The approach reflects a broader trend of treating black-box LLM judges as systems requiring calibration and audit rather than ground-truth oracles, relevant wherever LLM judges are used for model selection, content moderation, or automated grading in regulated contexts. A separate study examines LLM judges in a high-stakes technical domain: radiology report evaluation. The work finds that LLM-based evaluation metrics, which have mostly been validated on chest X-ray (CXR) reports, do not necessarily generalize to other imaging modalities and anatomical regions, and tests which model families, prompting strategies, and adaptation methods produce better-aligned judges across these settings, using two expert-annotated datasets spanning multiple modalities and anatomies (arXiv:2604.03376, Oct 16, replace version). This adds a domain-specific data point to the broader finding that LLM judges require calibration rather than blanket trust: robustness demonstrated in one evaluation domain (CXR reports) does not transfer automatically to adjacent domains (other modalities/anatomies). A third strand of work focuses on testing judge reliability itself rather than correcting bias post hoc. SANE (Scorer-specific Automated Metamorphic Testing) addresses a gap in existing LLM-judge reliability testing: current harnesses combine human-authored perturbation recipes with synthetic test generation, but leave the ideation of scorer-specific perturbation recipes — i.e., inputs expected to predictably shift a given judge's score along its specific scoring criterion — outside the automated pipeline, limiting testing to fixed, pre-defined perturbations (arXiv:2610.28886, Oct 22). This extends the calibration-and-audit framing of LLM judges toward automated, scorer-specific test generation. Separately, in the clinical domain, researchers proposed an alternative to LLM judges altogether rather than a correction method: Jev, a "decision language model" that returns structured judgments, was compared against frontier and open-weight LLM judges across four clinical AI evaluation use cases. On 118 pediatric answers, Jev's agreement with physicians' ordinal quality ratings (Gwet's AC2 0.50; expected score 0.53) matched or exceeded seven of 12 LLM judges tested, including Claude Opus 5.5 (0.49) and GPT-6 (0.38) (arXiv:2610.28826, Oct 22). This suggests that for some high-stakes domains, purpose-built structured-judgment models may offer a cheaper, faster alternative to LLM judges rather than merely a bias-correction layer on top of them. A fourth study turns to benchmark-level reliability rather than individual judge calibration or testing. Using 500 Arena-Hard v2.0 prompts with two models' answers held fixed, researchers ran GPT-4.1 as judge under all eight combinations of varying criteria, output format, and task framing, repeating each setup multiple times, and found that similar aggregate scores can conceal substantial changes in individual verdicts — both under exact repetition and under instruction changes (arXiv:2610.30512, Oct 23). This is a distinct finding from prior calibration/bias-correction work: it shows that aggregate benchmark scores (e.g., leaderboard-style win rates) can mask high variance and instruction-sensitivity at the level of individual pairwise verdicts, raising a reliability concern specifically for benchmarks like Arena-Hard that rely on aggregated LLM-judge comparisons. **Key Entities:** PRISM (arXiv:2610.18389); radiology LLM-judge generalization study (arXiv:2604.03376); SANE (arXiv:2610.28886); Jev decision language model (arXiv:2610.28826); Arena-Hard v2.0 judge-variance study (arXiv:2610.30512); GPT-4.1, Claude Opus 5.5, GPT-6 (as judges/evaluated models). **Implications:** Collectively, these studies argue against treating LLM-judge scores — whether individual verdicts or aggregate benchmark scores — as stable ground truth. This matters for AI governance and procurement decisions that rely on benchmark leaderboards, for regulated-domain deployments (clinical, legal, compliance) using LLM judges for grading or moderation, and for vendors selling evaluation-as-a-service. **Connections:** LLM benchmarking integrity; AI evaluation-as-a-service; clinical AI validation; Arena-Hard and leaderboard methodology; model selection pipelines.