The Silicon Mirror: Dynamic Behavioral Gating for Anti-Sycophancy in LLM Agents

arXiv cs.AI / 4/2/2026

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Key Points

The paper introduces “The Silicon Mirror,” an orchestration framework designed to reduce sycophancy in LLM agents by prioritizing epistemic accuracy over user validation pressures.
It uses three components—Behavioral Access Control (context gating via sycophancy risk scores), a Trait Classifier for persuasion tactics across multi-turn dialogue, and a Generator-Critic loop with an auditor veto and “Necessary Friction” rewrites.
In evaluations using Claude Sonnet 4 on 50 TruthfulQA adversarial scenarios, sycophancy drops from 12.0% (vanilla) to 4.0% (static guardrails) and further to 2.0% (Silicon Mirror), showing a large relative reduction.
Across-model testing with Gemini 2.5 Flash shows an even larger reduction in sycophancy (from 46.0% baseline to 69.6% reduction with the framework), supporting the approach’s effectiveness beyond a single model.
The authors argue that “validation-before-correction” is a distinct failure mode commonly associated with RLHF-trained models and that their dynamic gating/orchestration specifically targets it.

Abstract

Large Language Models (LLMs) increasingly prioritize user validation over epistemic accuracy-a phenomenon known as sycophancy. We present The Silicon Mirror, an orchestration framework that dynamically detects user persuasion tactics and adjusts AI behavior to maintain factual integrity. Our architecture introduces three components: (1) a Behavioral Access Control (BAC) system that restricts context layer access based on real-time sycophancy risk scores, (2) a Trait Classifier that identifies persuasion tactics across multi-turn dialogues, and (3) a Generator-Critic loop where an auditor vetoes sycophantic drafts and triggers rewrites with "Necessary Friction." In a live evaluation on 50 TruthfulQA adversarial scenarios using Claude Sonnet 4 with an independent LLM judge, we observe vanilla Claude sycophancy at 12.0% (6/50), static guardrails at 4.0% (2/50), and the Silicon Mirror at 2.0% (1/50)-an 83.3% relative reduction (p = 0.112, Fisher's exact test). A cross-model evaluation on Gemini 2.5 Flash reveals a higher baseline sycophancy rate (46.0%) and a statistically significant 69.6% reduction under the Silicon Mirror (p < 0.001). We characterize the validation-before-correction pattern as a distinct failure mode of RLHF-trained models.