Abstract: A computing system for monitoring language model compliance with a rubric of one or more output characteristics. The computing system includes processing circuitry configured to interface with a trained generative language model that receives input of a prompt including natural language text input and, in response, generates an output that includes natural language text output. The processing circuitry is further configured to monitor compliance of the generative language model with the rubric, by feeding the output of the generative language model to a rubric classifier configured to generate a predicted classification for an output characteristic in the rubric, and output the predicted classification.

Abstract: A computing system is provided for selective memory retrieval. The computing system includes processing circuitry configured to provide access to a plurality of memory banks, cause an interaction interface for a trained generative model to be presented, receive, via the interaction interface, an instruction from the user for the trained generative model to generate an output, extract a context of the instruction, generate a memory request including the context and the instruction, input the memory request into a plurality of memory retrieval agents respectively coupled to the plurality of memory banks to retrieve a plurality of relevant memories, generate a prompt based on the retrieved relevant memories and the instruction from the user, provide the prompt to the trained generative model, receive, in response to the prompt, a response from the trained generative model, and output the response to the user.

Abstract: A computing system is provided, comprising at least one processor configured to receive a user interaction history of a user, extract memories from the user interaction history, consolidate the memories into memory clusters, cause a prompt interface for a trained model to be presented, receive, via the prompt interface, an instruction from the user for the trained model to generate an output, generate a prompt based on the memory clusters and the instruction from the user, provide the prompt to the trained model, generate, in response to the prompt, a response via the trained model, and output the response to the user.

Abstract: According to one aspect, a computing system is provided that includes processing circuitry configured to receive input data from multiple interaction modalities of a user, generate a multi-interaction-modality user interaction history from the input data, and extract memories from the multi-interaction-modality user interaction history using a trained memory-extracting generative model. The memories include natural language text descriptions of interactions in the user interaction history generated by the trained memory-extracting generative model. The processing circuitry is further configured to store the memories in file storage having an associated database with a vector search interface configured to receive memory retrieval queries.

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: A reference to a digital item is stored as a digital card. The digital card can also be contained in, and/or refer to, other digital cards. The digital card can also include properties or attributes that may be added from the digital item that is being referred to. The digital card can be stored in a data pod within a de-centralized data storage system architecture.