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LLMs can audit clinical prediction networks via computation alone, not weight access

Researchers tested whether frontier LLMs (GPT-6 Astra and others) can audit small clinical prediction networks by inspecting their weights or using a calculation tool. Without tools, Astra succeeded on 353-weight networks but failed at 3,457 weights; with a tool that exposed predictions (black-box) or weights (white-box), it reliably recovered planted relationships and detected biased subgroups in networks up to 38,401 weights. The study concludes that for auditing these networks, computation and elicitation matter, not access to the weights.