arXiv — cs.AI preprintsInternational9 October 2026
All Verdicts are Not Equal: Rethinking LLM Judge Reliability
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.12083v1 Announce Type: cross Abstract: LLM-as-a-Judge is the standard paradigm for NLP evaluation, yet its systemic reliability remains poorly understood despite being widely treated as a deterministic ground truth. We present a comprehensive reliability audit, stresstesting six frontier models across four benchmarks, five prompt formats, two presentation orders, three sampling temperatures, and ten repetitions per condition. Our empirical analysis reveals severe vulnerabilities: verdicts change across identical replications at temperature zero, position-order swaps flip the majorit
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment9 October 2026
- Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction9 October 2026
- Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice9 October 2026
- Verification and Self-Improvement in Agentic AI: Foundations and Limits9 October 2026
- The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate9 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.