AINeutralarXiv – CS AI · Mar 36/106
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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.
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