EviBound Framework Eliminates False Claims in Autonomous AI Research

Cornell University researcher Ruiying Chen has introduced EviBound, an innovative governance framework designed to eliminate false claims made by LLM-based autonomous research agents. The framework addresses the issue of 'hallucinations,' where AI systems report task completion without providing verifiable evidence. EviBound employs a dual-gate architecture that mandates machine-checkable evidence for every claim. The framework's pre-execution Approval Gate validates acceptance criteria schemas before code runs, proactively identifying structural violations. Complementing this, the post-execution Verification Gate validates artifacts using MLflow API queries, ensuring that claimed results are backed by queryable run IDs, required artifacts, and a 'FINISHED' status. Bounded, confidence-gated retries also help recover from transient failures without leading to unbounded loops. In benchmark tests spanning infrastructure validation, ML capabilities, and governance stress tests, EviBound demonstrated its effectiveness. While a prompt-level baseline resulted in 100% hallucination, and a verification-only baseline reduced it to 25%, EviBound achieved 0% hallucination with only an 8.3% execution overhead. This research emphasizes that architectural enforcement is crucial for research integrity, achievable through governance gates rather than relying solely on model scale. The framework includes execution trajectories, MLflow run IDs for verified tasks, and a verification protocol. This allows independent validators to re-run verification on the same run IDs.