The Groundhog Trap – Multi-model consensus and AI output failover framework (github.com)

The Groundhog Trap is an innovative AI governance framework introduced by Ricky Rojas in 2026, aimed at enhancing trust and reliability in large language models (LLMs). By employing a multi-model consensus approach, it routes prompts through multiple independent LLMs, validates their outputs through adversarial techniques, and ensures decision-making is deterministic. This prototype features a three-model symmetric ensemble, consensus scoring, and mechanisms for audit logging and hallucination detection, ensuring outputs are not only accurate but also verifiable and trustable. This development is significant for the AI/ML community as it directly addresses concerns around model reliability and ethical transparency in AI applications. By facilitating a structured process for output comparison and validation, The Groundhog Trap paves the way for more responsible AI deployment in enterprise settings. Future enhancements planned for the framework include adaptive consensus thresholds and risk-based routing which will further strengthen AI governance and operational safety, positioning it as a potential standard for trustworthy AI systems in various industries.