arXiv:2606. 30931v1 Announce Type: new Abstract: The LLM Jury, a Panel of LLM Evaluators (PoLL) reporting consensus scores, has become a practical alternative to single-judge LLM evaluation, yet its statistical behavior remains poorly understood.
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arXiv:2608. 06940v1 Announce Type: new Abstract: LLM judge panels are a standard evaluation tool, but prior work reports highly correlated panel errors: nine judges provide roughly the effective information of two independent ones, and aggregation closes only a small fraction of the gap.
The paper proposes a transparent, user‑configurable rule for selecting arguments in deliberative polls, replacing opaque learned rankers. It formalises argument selection over bipolar justification sets, introduces seven civic recommender criteria, and presents a one‑hop reversed endorsement flow rule that meets them. Experiments on 17,000 simulated runs show the rule performs comparably to random on coverage but outperforms other methods on endorsement mass and robustness under adversarial pressure.
JuryProbe is an empirical diagnostic tool designed to assess consensus risk in panels of reference‑free large language model judges used for factuality verification. It estimates risk by measuring false‑negative correlations and false‑consensus lift from a labeled calibration probe, and routes high‑risk majority decisions to judges with trusted references. The approach was validated on FEVER corruptions, showing that flagged decisions can be grounded without additional reference acquisition in most cases, while reducing false accepts by about 0.4% and avoiding 28% of reference acquisitions.
arXiv:2609.39229v1 Announce Type: cross Abstract: Automatic evaluation of faithfulness increasingly relies on a large language model acting as a judge, yet the most reliable judges are proprietary fr...
arXiv:2608. 06422v1 Announce Type: new Abstract: Giving an LLM judge more compute does not necessarily make it check more requirements.
The paper introduces the Wiggle Framework, a unified stress test for assessing epistemic stability in large language model (LLM) judges. It evaluates judge robustness across three dimensions—Mechanical Consistency, Single-turn Conviction, and Multi-turn Persistence—using 9 frontier models on 14 judging tasks related to safety, toxicity, AI writing detection, and political-response evaluation. Results show significant instability, with verdict flips ranging from 25–71% under static pushback and 62–91% when challenged by an adversarial LLM, and highlight that successful pressure often misaligns with ground truth.