Abstract: An example provides a multi-agent system. Via an orchestrator agent, a query input is received. Via the orchestrator agent, the query input and a first instruction are provided to a generative machine learning model (GMLM). The first instruction is to cause the GMLM to determine queries using the query input. Via a query execution agent, the queries execute in parallel. Via the orchestrator agent, it is determined whether the queries are executing. Via a query evaluation agent, query results of execution of the queries and a second instruction are provided to the GMLM. The second instruction is to cause the GMLM to generate, for each query result, a query result summary. Via the orchestrator agent, the query result summaries are used to determine a subset of the query results for presentation via a device.

Abstract: Embodiments of the disclosed technologies are capable of training a large language model (LLM) to perform a first task type associated with a first task type using a first prompt comprising a task reasoning and an instruction associated with the task. The task reasoning comprises a set of guidelines associated with the task. The embodiments describe executing the LLM to perform the first task type. Performing the first task type comprises the LLM generating an output using the set of guidelines associated with the task. The embodiments describe executing the LLM to perform a second task type associated with the task using a second prompt. The second prompt comprises the instruction associated with the task.

Abstract: Embodiments described herein are capable of providing synthesized and personalized supplemental information to a user. The embodiments describe determining, based on a search result selected by a user, using a LLM, a first set of categories, a first set of keywords, a second set of categories, and a second set of keywords. The embodiments further describe retrieving a set of digital content items. A first digital content item of the set of digital content items is retrieved based on a first category of the first set of categories, and a second digital content item of the set of digital content items is retrieved based on a first category of the second set of categories. The embodiments further describe generating, using a LLM, a summary of one or more digital content items of set of digital content items. The embodiments further describe causing the summary to be displayed on a device.

Abstract: In an example embodiment, user interactions with a graphical user interface are modeled to derive an efficient representation that is highly available through a framework. This representation enables downstream analysis as to the relevancy of the user interactions through libraries leveraging standardized activity representations. With these components, it becomes possible to derive user intent in a modular fashion, domain by domain, while decoupling many system aspects, and also providing high capacity and precise intent information to leverage for personalization.

Abstract: In an example embodiment, user interactions with a graphical user interface are modeled to derive an efficient representation that is highly available through a framework. This representation enables downstream analysis as to the relevancy of the user interactions through libraries leveraging standardized activity representations. With these components, it becomes possible to derive user intent in a modular fashion, domain by domain, while decoupling many system aspects, and also providing high capacity and precise intent information to leverage for personalization.