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HARDEN creates harder AI evaluation cases without changing the expected answer

HARDEN creates harder AI evaluation cases without changing the expected answer

The preprint introduces an evolutionary search method that modifies existing evaluation cases. Across three benchmarks and three Qwen3.5 model scales, its authors report an average accuracy reduction of 22.7%, though the abstract does not provide enough detail to audit the calculation or determine how closely the generated cases represent real-world situations.

ASIRF proposes adapting sensitive-data redaction to context without retraining the model

ASIRF proposes adapting sensitive-data redaction to context without retraining the model

A preprint describes a system that retrieves domain-specific definitions of sensitive information at inference time. Its initial results favor its detection recall over a reference classifier in most tested combinations, although the information available does not make it possible to assess all error types or its readiness for real-world use.