Abstract: A model compression system can compress the functionality of large language models into small language models. The model compression system defines a process such as a set of steps that implement an operation. The model compression system also defines a set of use-cases implementing the process and a reward function that quantifies an implementation of the process against target benchmarks. The model compression system finetunes the small language model using the defined process, the use-cases, and the reward function enabling the small language model to execute the process with the same accuracy and precision as the large language model. The small language model may be validated to verify accuracy, precision, drift, etc. and placed in production.

Abstract: Embodiments relate to a computer-implemented method including receiving an instruction message for an automated agent to enter a processing state. The method may include analyzing the instruction message to detect that a task is to be performed. Analyzing the instruction message may include determining that performing the task requires additional information. Determining includes classifying the automated agent in a standby state. The method may include generating a survey message in the real-time chat interface to request additional information from the set of users. The method may also include processing responses from the set of users to the survey message in the real-time chat interface. The method may further include adding a task message to the real-time chat interface. The method may not include a request from the set of users to perform the task using the additional information, thereby improving efficiency of the real-time chat interface.

Abstract: An AI Agent Platform can facilitate execution of tasks. The AI Agent Platform may receive a request to execute a task from a computing device. The AI Agent Platform may classify an intent associated with the request. The AI Agent Platform may then execute a specialist module using an object derived from the request and the intent. The specialist module defines a sequence of subtasks that implement the task. The specialist module invokes one or more agent modules that are each configured to implement subtasks of the sequence of subtasks. The AI Agent Platform facilitates a presentation of a task response corresponding to execution of the task.

Abstract: Systems and methods are implemented to provide a framework for generating artificial intelligence-based automated agents according to graphical representations of artificial intelligence processes and connections amongst these processes, which collectively define the functions of the artificial intelligence-based automated agents. Through a graphical user interface, these functions are dynamically evaluated and tested to identify any potential errors associated with the automated agents and to provide data corresponding to any intermediate operations performed by these automated agents in order to generate desired outputs. Once an automated agent is defined through the graphical user interface, the automated agent can be generated and invoked, such as through endpoint application programming interfaces (APIs).

Abstract: An AI Agent Platform implements self-healing within AI modules for fault tolerant task execution. The AI Agent Platform may receive a task and an identification of an intent associated with the task. The AI Agent Platform may execute a specialist module using an object derived from the task and the intent. The specialist module defines a sequence of subtasks configured to implement the task. The specialist module can invoke an agent module to execute a first subtask and analyzes a resulting state of the task in response to executing the first subtask. If the state of the task indicates a failure condition, the specialist module may be executed again using a new object derived from the task and the state of the task to generate a new sequence of tasks that extend from the first subtask. The specialist module invokes a second agent module to execute the second subtask.

Abstract: An AI Agent Platform autonomously processes data streams for event implementation. The AI Agent Platform receives a real-time data stream associated with a profile. The AI Agent Platform defines a dataset from the data stream by dynamically sampling the data stream according to a time window selected using the profile. The AI Agent Platform may detect a correspondence between the dataset and an event registry. The event registry may include proactive events having a predicted significance for a user. The AI Agent Platform may then execute a machine-learning model using an event, the dataset, and the profile to define an implementation of the event for the user. Upon receiving authorization to execute the event, the AI Agent Platform facilitates execution of the implementation of the event.

Abstract: Systems and methods for automatically and continuously processing communications exchanged among users and automated agents and corresponding user behaviors are provided to dynamically generate topical communications sessions from ongoing communications sessions. The topical communications sessions are implemented to reduce the cognitive effort of conversation management within existing communications sessions where topical drift may occur. The framework allows users to submit multimedia inputs that are classified by the systems and methods and automatically routed to appropriate communications containers.

Abstract: Techniques for artificial intelligence systems to provide customized services, tasks, and recommendations to related user devices based on data streams. In an example, a method may obtain one or more data streams received during one or more ongoing communications sessions among a set of users and an automated agent, wherein the one or more data streams are converted by one or more large language models (LLMs) into a set of key-value pairs of user attributes, wherein the set of key-value pairs are used to generate merged attributes corresponding to the set of users, and wherein the merged attributes are used to update a set of profiles. A method may update one or more user interfaces associated with a user of the set of users to facilitate the one or more ongoing communications sessions using the merged attributes.

Abstract: Systems and methods for automatically and continuously evaluating user data collected through different communications sessions and other data sources to perform user profiling and define familiar relationships amongst users.

Abstract: A flexible solid-state resistor comprises a string of ceramic resistors that can be used to charge the capacitors of a linear transformer driver (LTD) used in a pulsed power machine. The solid-state resistor is able to absorb the energy of a switch prefire, thereby limiting LTD cavity damage, yet has a sufficiently low RC charge time to allow the capacitor to be recharged without disrupting the operation of the pulsed power machine.

Abstract: A flexible solid-state resistor comprises a string of ceramic resistors that can be used to charge the capacitors of a linear transformer driver (LTD) used in a pulsed power machine. The solid-state resistor is able to absorb the energy of a switch prefire, thereby limiting LTD cavity damage, yet has a sufficiently low RC charge time to allow the capacitor to be recharged without disrupting the operation of the pulsed power machine.