Abstract: In accordance with examples of the present disclosure, a collaborative platform provides a digital collaboration assistant that continuously monitors and analyzes shared meeting contents (e.g., voice, text chat messages, shared links and documents, presentation materials, and the like) by participants during a collaborative meeting in near real-time, periodically updates a structure summary log of the meeting contents that are deemed important during the collaborative meeting, and interacts with the participants throughout the collaborative meeting in near real-time, for example, to answer questions or provide additional information.

Abstract: Disclosed is a system for creating solution plans to solve problems in an AI system. An example system includes a large language model (LLM), a plan creation component, a plan working memory, and a plan execution component. The plan creation component leverages the power of the LLM to break problems into sets of discrete tasks, or solution plans, which are stored in the plan working memory. As each step of a solution plan is executed by the plan execution component, results are captured in the plan working memory so that the last executed step is captured. The working memory operates in the background of the AI system to ensure that the discrete tasks are executed, managed, and tracked until a complete solution is realized. The self-maintained working memory topology provides a solution to problems areas often encountered in conventional stateless AI system that encounter token limits in problem solving.

Abstract: In accordance with examples of the present disclosure, a collaborative platform provides a digital collaboration assistant that continuously monitors and analyzes shared meeting contents (e.g., voice, text chat messages, shared links and documents, presentation materials, and the like) by participants during a collaborative meeting in near real-time, periodically updates a structure summary log of the meeting contents that are deemed important during the collaborative meeting, and interacts with the participants throughout the collaborative meeting in near real-time, for example, to answer questions or provide additional information.

Abstract: Aspects of the present application relate to machine learning (ML) structured result generation. In examples, an instruction of programmatic code that invokes an ML model indicates a result interface in which model output is to be stored. The result interface is processed to generate a data format description for the result interface, such that the input to the ML model further includes the data format description. As a result of providing the data format description as input to the ML model, the ML model is induced to generate structured model output that corresponds to the result interface. The resulting model output is processed to generate an instance of the result interface, for example having one or more corresponding properties from the structured model output. Accordingly, the programmatic code is able to reliably perform subsequent processing based on the generated instance of the result interface.