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Language grounding increasingly involves non-text observations whose structure is not naturally expressed as words or objects. We study this problem for physiological time series in non-contact blood pressure (BP) estimation: remote photoplethysmography (rPPG) provides measured evidence about bodily state, but numerical pipelines expose little semantic structure about why a window is reliable or how its cues should be fused.
Large language models (LLMs) increasingly support biomedical research, yet their behavior under adversarial requests for dual-use knowledge remains insufficiently characterized. We introduce BioDisclose, a benchmark for measuring biomedical knowledge disclosure under adversarial elicitation.
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