Self-Anchoring Calibration Drift in Large Language Models: How Multi-Turn Conversations Reshape Model Confidence

Researchers identified Self-Anchoring Calibration Drift (SACD), where large language models show systematic confidence changes when building on their own outputs in multi-turn conversations. Testing Claude Sonnet 4.6, Gemini 3.1 Pro, and GPT-5.2 revealed model-specific patterns, with Claude showing decreasing confidence and significant calibration errors, while GPT-5.2 exhibited opposite behavior in open-ended domains.