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METR · March 11, 2025

Reasoning oversight requires testing legibility and faithfulness separately

analysis Model EvaluationAI Red Teaming
Why it matters

METR’s March 2025 analysis distinguishes readable reasoning from reasoning that reliably reflects a model’s decision process. It explains how traces can help diagnose failures, investigate reward hacking and assess apparent underperformance, while noting that evidence for faithfulness remains limited. The recommendations include tracking reasoning properties in system cards and scrutinizing training pressure that removes undesirable-looking reasoning without changing behavior. This is an oversight proposal supported by examples and prior research, not a validated guarantee that transparent traces reveal every hidden objective or prevent misconduct.

My takeaway: Evaluate readability, behavioral informativeness and adversarial robustness as separate properties. Preserve appropriate access for oversight and recheck monitors after training changes that compress, hide or reshape reasoning traces.
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