New framework called Silicon Mirror reduces AI sycophancy by 83% through dynamic behavioral controls and critic auditing loops.

Silicon Mirror framework addresses sycophancy in LLMs by detecting user persuasion tactics and adjusting AI responses to prioritize factual accuracy over user validation

Three-component architecture includes Behavioral Access Control system for real-time risk scoring, a Trait Classifier for identifying persuasion tactics, and a Generator-Critic loop with auditor vetoes

Testing on Claude Sonnet 4 with 50 TruthfulQA adversarial scenarios showed sycophancy reduced from 12.0% (vanilla) to 2.0% with Silicon Mirror—an 83.3% relative improvement over static guardrails at 4.0%

Framework uses 'Necessary Friction' mechanism to trigger rewrites of sycophantic responses and maintain epistemic integrity in multi-turn dialogues