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The Confidence Trap occurs when we trust one model’s output simply because it sounds certain. Across 1,324 turns in our April 2026 audit, we found that cross-referencing Anthropic and OpenAI prevents errors that solo models miss. While we hit 99
The Confidence Trap is the risk of trusting one model implicitly. Even top performers like OpenAI and Anthropic struggle with hallucinations. Our April 2026 audit of 1,324 turns found 99.1% signal detection, yet 0
The "Confidence Trap" happens when we trust a single LLM blindly, ignoring latent errors. In our April 2026 audit of 1,324 turns, we achieved 99.1% signal detection but caught 0.9% silent turns. Relying solely on OpenAI or Anthropic is a risk
The Confidence Trap occurs when we mistake LLM fluency for facts. Our April 2026 audit of 1,324 turns yielded 99.1% signal detection via multi-model review, capturing 0.9% silent turns where models failed