Abstract: Techniques for generating a prompt for a language model to determine an action responsive to a user input, are described. In some embodiments, the system receives a user input, determines one or more application programming interfaces (APIs) configured to perform actions that are relevant to the user input and exemplars representing examples of using the APIs with respect to user inputs similar to the current user input. The system further determines device states of devices that are determined to be related to the user input and also determines other contextual information (e.g., weather information, time of day, geographic location, etc.). The system generates a prompt including the user input, the APIs, the exemplars, the device states, and the other contextual information. A language model processes the prompt to determine an action responsive to the user input and the system causes performance of the action.

Abstract: Techniques for generating a prompt for a language model to determine an action responsive to a user input, are described. In some embodiments, the system receives a user input, determines one or more application programming interfaces (APIs) configured to perform actions that are relevant to the user input and exemplars representing examples of using the APIs with respect to user inputs similar to the current user input. The system further determines device states of devices that are determined to be related to the user input and also determines other contextual information (e.g., weather information, time of day, geographic location, etc.). The system generates a prompt including the user input, the APIs, the exemplars, the device states, and the other contextual information. A language model processes the prompt to determine an action responsive to the user input and the system causes performance of the action.

Abstract: Techniques for generating a prompt for a language model to determine an action responsive to a user input, are described. In some embodiments, the system receives a user input, determines one or more application programming interfaces (APIs) configured to perform actions that are relevant to the user input and exemplars representing examples of using the APIs with respect to user inputs similar to the current user input. The system further determines device states of devices that are determined to be related to the user input and also determines other contextual information (e.g., weather information, time of day, geographic location, etc.). The system generates a prompt including the user input, the APIs, the exemplars, the device states, and the other contextual information. A language model processes the prompt to determine an action responsive to the user input and the system causes performance of the action.

Abstract: A cost function is specified to optimize a combination of N drugs, where the cost function includes at least one phenotypic contribution corresponding to efficacy and at least one phenotypic contribution corresponding to safety, and at least one of the N drugs is a nanomaterial-modified drug, with N being 2 or more. In vitro or in vivo tests are conducted by applying varying combinations of dosages of the N drugs to determine the phenotypic contributions from results of the tests. The results of the tests are fitted into a representation of the cost function, and, using the representation of the cost function, at least one optimized combination of dosages of the N drugs is identified.

Abstract: A system includes a drug database, a disease gene database, and a network model describing a physiological or biological network. The network model receives drug data from the drug database related to drugs used in an experiment, and receives disease gene data from the disease gene database related to subjects analyzed in the experiment. The network model identifies propagation of drugs and disease through the physiological or biological network from the drug data and the disease gene data, and outputs a set of system response predictors based on the identification of the propagation. The system further includes a predictive module that receives the system response predictors, receives result data related to outcomes of the experiment, and generates a system response model based on the system response predictors and the result data.