When Does a Second Model Help? Cross-Model Review in LLM Verification

arXiv:2610.01471 · cs.CL, cs.AI, cs.SE · Submitted 2026-10-01 · Read on arXiv

cs.CL, cs.AI, cs.SE

Submitted: 2026-10-01

Updated: 2026-10-06

Terminology

• Verification — 11× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• F1 Score — 4× in this paper · explained in 3 episodes
• Provenance — 4× in this paper · explained in 3 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Sycophancy — 3× in this paper · explained in 4 episodes
• Verifier — 3× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Hallucination Detection — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Provenance — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Multi-Agent System — 1× in this paper · explained in 5 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Unique Finding Rate — 1× in this paper

Sources

• Correlated Errors in Large Language Models
• LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
• Automating Code Review Activities by Large-Scale Pre-training
• Cross-Context Review: Improving LLM Output Quality by Separating Production and Review Sessions
• Cross-Context Verification: Hierarchical Detection of Benchmark Contamination through Session-Isolated Analysis
• More Rounds, More Noise: Why Multi-Turn Review Fails to Improve Cross-Context Verification

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