bias-auditing

A research paper introducing Chain-of-Models (CoM), an automated pipeline where a second LLM audits a first model's reasoning trace to correct cognitive biases. It finds that auditor effectiveness depends on model family and bias type, and proposes a bias-specific auditor selection rule that improves judgment accuracy.

This paper audits the reliability of LLM-as-judge evaluation by showing that changing the evaluator model can shift scores even when candidate responses are fixed, and it examines scaling and upgrade paths for Qwen3 and MiniMax models, concluding that judge upgrades are not interchangeable and proposing best practices for reporting.

This research paper investigates position bias in reasoning models, finding that bias scales with the length of the reasoning trajectory rather than being eliminated by 'more thinking.' The study provides causal evidence and a diagnostic toolkit for auditing this length-driven bias in multiple-choice QA evaluations.