EviBound: New Governance Framework Eliminates False Claims in Autonomous AI Research
Published on October 28, 2025 at 05:00 AM
Cornell University researcher Ruiying Chen has introduced EviBound, an innovative governance framework designed to eliminate false claims in autonomous research systems. EviBound tackles the issue of AI agents confidently reporting incorrect or unsubstantiated results by implementing dual governance gates that require machine-checkable evidence for every claim.
The framework features an Approval Gate that validates acceptance criteria before code execution, proactively preventing structural violations. Following execution, a Verification Gate validates artifacts using MLflow API queries, ensuring that reported results are supported by queryable run IDs, required artifacts, and a 'FINISHED' status. Bounded retry mechanisms allow for recovery from transient failures without creating infinite loops.
In benchmark tests across eight tasks, EviBound achieved a 0% hallucination rate, a significant improvement over baseline systems that either lacked governance (100% hallucination) or relied solely on post-execution verification (25% hallucination). EviBound verified 7 out of 8 tasks, with the Approval Gate correctly blocking one malformed task. The results indicate a clear progression: hallucination drops from 100% to 25% to 0% as architectural enforcement increases.
The research emphasizes that AI research integrity is an architectural property achieved through governance gates rather than simply relying on model scale or prompt engineering. This new approach offers a pathway towards more trustworthy and reproducible autonomous research.