Zhiyu Liu

Clinical prediction models often arrive as files without the data they were trained on, so an auditor must work out what the model actually computes. Researchers trained tanh networks of 105 to 38,401 weights on simulated subarachnoid haemorrhage cohorts, planted known relationships or a label bias, and asked GPT-6 Astra to answer with no tools, with a calculation-only white-box tool, or with a black-box tool exposing only predictions. Without tools Astra recovered planted relationships in 353-weight networks but became unreliable at 3,457 weights; with the calculator it recovered the relationship in all 29 runs that had one, raised no false alarm in 15 null runs, and black-box matched white-box.

Clinical prediction models often arrive as files without the data they were trained on, so an auditor must work out what the model actually computes. Researchers trained tanh networks of 105 to 38,401 weights on simulated subarachnoid haemorrhage cohorts, planted known relationships or a label bias, and asked GPT-6 Astra to answer with no tools, with a calculation-only white-box tool, or with a black-box tool exposing only predictions. Without tools Astra recovered planted relationships in 353-weight networks but became unreliable at 3,457 weights; with the calculator it recovered the relationship in all 29 runs that had one, raised no false alarm in 15 null runs, and black-box matched white-box.