Abstract: Computer-implemented systems and methods are disclosed, including systems and methods for automatically solving problems. A computer-implemented method may include: by an agent service configured to interact with an LLM to complete a run: providing an LLM with access to a state machine, executing an initial state of the state machine with the LLM, determining a subsequent state of the state machine based on at least an initial LLM output, and executing the subsequent state of the state machine.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods for automatically solving computational tasks or problems. A computer-implemented method may include: providing an agent service that utilizes a plurality of agents to process one or more tasks; receiving, by a first agent, a request to handle a first task; obtaining, by the first agent, a first accessory to handle the first task; assigning, by the first agent, at least a portion of the first task to a second agent; sharing, by the first agent, the first accessory to the second agent; and processing, by the second agent, at least the portion of the first task using the first accessory to generate a processing result.

Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for generating data objects and/or updating an ontology. A computer-implemented method may include: employing one or more large language models (“LLMs”) to generate at least a data triple and a classified triple; executing, using the classified triple, a similarity search with reference to an ontology to determine that the classified triple at least partially matches one or more data object types defined in the ontology; in response to the determination, adding into a first database at least a first data object of a first data object type that represents a first entity in the data triple and a second data object of a second data object type that represents a second entity in the data triple.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for searching a large corpus of data. A computer-implemented method may include: receiving a first user input comprising a natural language query; vectorizing the first user input into a query vector; executing, using the query vector, a similarity search in a document search model to identify one or more similar document portions, where the document search model includes a plurality of vectors corresponding to a plurality of portions of a set of documents; generating a first prompt for a large language model (“LLM”), the first prompt including at least the first user input, and the one or more similar document portions; transmitting the first prompt to the LLM; receiving a first output from the LLM in response to the first prompt; and providing, via a user interface, the first output from the LLM.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for creating and/or updating an ontology. A computer-implemented method may include: receiving tabular data from one or more data sources; generating an interactive graphical representation of at least a portion of the tabular data and connections between the portion of the tabular data; providing, via a user interface, the interactive graphical representation; receiving a user operation via the user interface, updating an ontology and/or generating transformations for adding data objects into a database.

Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.

Abstract: A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.