Scalable watermarking for identifying large language model outputs.

Large language models (LLMs) have enabled the generation of high-quality synthetic text, often indistinguishable from human-written content, at a scale that can markedly affect the nature of the information ecosystem. Watermarking can help identify synthetic text and limit accidental or deliberate misuse, but has not been adopted in production systems owing to stringent quality, detectability and computational efficiency requirements. Here we describe SynthID-Text, a production-ready text watermarking scheme that preserves text quality and enables high detection accuracy, with minimal latency overhead.

Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians.

Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings, but can make errors in cases accurately diagnosed by clinicians and vice versa. We developed Complementarity-Driven Deferral to Clinical Workflow (CoDoC), a system that can learn to decide between the opinion of a predictive AI model and a clinical workflow. CoDoC enhances accuracy relative to clinician-only or AI-only baselines in clinical workflows that screen for breast cancer or tuberculosis (TB).

Proton interaction in phosphate adsorption onto goethite.

The adsorption of phosphate on goethite is generally modeled by assuming a simple ligand exchange reaction with surface hydroxyl groups. This study investigates the binding forms of phosphate on goethite by evaluating the proton interaction and surface charge change during phosphate adsorption. It is found that OH(-) release stoichiometry increases with phosphate coverage, which suggests that different mechanisms predominate at different phosphate loadings.

Slow adsorption reaction between arsenic species and goethite (alpha-FeOOH): diffusion or heterogeneous surface reaction control.

The slow stage of phosphate or arsenate adsorption on hydrous metal oxides frequently follows an Elovich equation. The equation can be derived by assuming kinetic control by either a diffusion process (either interparticle or intraparticle) or a heterogeneous surface reaction. The aim of this study is to determine whether the slow stage of arsenic adsorption on goethite is more consistent with diffusion or heterogeneous surface reaction control.

Evidence for surface precipitation of phosphate on goethite.

Recent studies have suggested that the interaction between phosphate and goethite includes ternary adsorption/ surface precipitation as well as surface complexformation. The ternary adsorption/surface precipitation process envisioned involves the dissolution of the goethite crystal and subsequent adsorption of iron on the surface-bound phosphate. Further evidence to support the suggested process is needed.