Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

Abstract: Systems and methods for generating predicted end-to-end cyber-security attack characteristics via bifurcated machine learning-based processing of multi-modal data are disclosed. The system accesses multi-modal data indicating a set of security information related to a computing system. The system then generates a set of extracted characteristics indicating a cyber-security attack on the computing system, via a supervised machine learning model, using the multi-modal data. Using this information, the system generates a revised set of extracted characteristics indicating the cyber-security attack, via an unsupervised machine learning model, using the extracted set of characteristics indicating the cyber-security attack, where the revised set of characteristics includes at least one new characteristic that was not included in the extracted set of characteristics indicating the cyber-security attack on the computing system.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

Abstract: Systems and methods for detecting anomalies in generative outputs are disclosed herein. The system receives a user prompt indicating a request for data over a time period. The system inputs, into a model, the user prompt to cause the model to generate an output based on the user prompt. The system then generates the first tokens based on the output. To generate the second tokens, the system retrieves, based on the user prompt, sources relating to the data requested by the user prompt. The system then generates queries to request, from the sources, the data over the time period and generates the second tokens based on the retrieved data. The system then performs a comparison of the first tokens and the second tokens and accepts or rejects the output of the model based on the comparison.

Abstract: Described herein are systems and methods for discovering and proactively mitigating previously unknown security vulnerabilities. The systems and methods herein can utilize security vulnerability information to discover potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.

Abstract: Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.

Abstract: Presented herein are systems and methods for aggregating data from disparate sources using generative models. A computing system may retrieve, from a plurality of data sources, a first plurality of datasets for at least one of a plurality of features for a network environment over a first time period. The computing system may execute a first generative model using the first plurality of datasets to create a second plurality of datasets. The computing system may identify, from the second plurality of datasets, at least one dataset corresponding to a feature of the plurality of features. The one or more processors may execute select at least one machine learning model based on the feature. The computing system may execute the at least one ML model using the dataset to determine an output. The computing system may cause displaying of a visualization of the output via a graphical user interface.

Abstract: The systems and methods disclosed herein provide a lightweight artificial intelligence agent router that dynamically allocates tasks across a plurality of stateless AI agents. The router receives a task request and classifies it into one or more task types by comparing the request against historical task-agent pairings stored in a datastore. Each agent is registered in an agent registry that includes a metadata profile with performance metrics derived from prior executions. The router scores each agent by evaluating these performance metrics against the classified task type to rank the agents and select a subset of the agents whose scores satisfy a threshold value. The router determines an execution order by evaluating dependencies between sequential portions of the task request, thereby ensuring that outputs from earlier agents are available as inputs to subsequent agents. After task completion, the router updates the agent registry with execution results to adjust future routing decisions.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.

Abstract: The present disclosure relates to systems, methods, and devices for providing a centralized data store that receives and transmits data via agents that operate independently of one another. In some implementations, the agents transform data prior to transmission to the centralized data store. In some implementations, the centralized data store is configured to transform data. The centralized data store can evaluate, route, score, and otherwise manipulate and select received data. The centralized data store can respond to data requests based at least in part on its evaluation of received data.

Abstract: The present disclosure relates to systems, methods, and devices for providing a centralized data store that receives and transmits data via agents that operate independently of one another. In some implementations, the agents transform data prior to transmission to the centralized data store. In some implementations, the centralized data store is configured to transform data. The centralized data store can evaluate, route, score, and otherwise manipulate and select received data. The centralized data store can respond to data requests based at least in part on its evaluation of received data.

Abstract: Systems, methods, and devices for facilitating computational resource access by artificial intelligence (AI) agents through token-based allocation and multi-agent workflow optimization. The system generates tokens corresponding to computational resources including processing power, memory, storage, and bandwidth. AI agents submit resource requests with priority tokens, creating queues ordered by priority token quantity. Higher-priority bids receive preferential positions. The system transfers resource tokens to agents based on queue order, enabling resource access through token exchange. The system can receive user prompts indicating computational objectives and determines multiple AI agentic approaches comprising AI model sequences. After evaluating approaches against operational policies, the system generates resource utilization and performance estimates, executing a preferred approach balancing efficiency with output quality.

Abstract: A distributed ledger technology application can establish a first connection with a computing application executed by a banking computing infrastructure and a second connection with one or more nodes hosting a distributed ledger via a second communication protocol. The application can receive, from a client device through the through the first connection using the first communication protocol, a receive instruction to complete an asset transfer to transfer an asset from a source account hosted in the banking computing infrastructure to a DLT-based client account. The application can execute an asset conversion agent to determine whether any asset conversion is required. The application can execute an instruction generation agent to generate a new receive instruction. The application can execute a ledger agent to generate an instruction to cause the one or more nodes to add a record of the asset transfer to the distributed ledger.

Abstract: Systems and methods may validate operation request data associated with operations requested or triggered by maker-user inputs. A computing device can execute software routines and/or one or more machine-learning architectures to obtain one or more operation records for an operation from one or more data sources; extract a feature vector for the operation based upon a plurality of operation features extracted using the one or more operation records for the operation; determine an operation type for the operation by applying a classifier of a machine-learning architecture on the feature vector for the operation; generate a risk score for the operation by applying a risk model of the machine-learning architecture on the operation feature vector and the operation type; determine one or more authorization thresholds for the operation based upon the risk score; and transmit the operation record to one or more checker client devices corresponding to the authorization thresholds.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

Abstract: Presented herein are system and methods for configuring data records of application programming interface (API) specifications for applications in network environments. A service may retrieve data associated with a first API for a first plurality of functions. The service may execute a generative model using the data associated with the first API to generate a first plurality of embeddings corresponding to a first API specification. The service may identify, from a database, a second plurality of embeddings corresponding to a second API specification for a second API that defines a second plurality of functions available for invocation to the one or more applications. The service may determine a redundancy between the first API specification and the second API specification using the first plurality of embeddings and the second plurality of embeddings. The service may select an API from one of the first API and the second API.

Abstract: Presented herein are systems and methods for integrating model context protocols (MCPs) for use in network environments. A server may retrieve a data file comprising an MCP schema defining a plurality of functions of a MCP for a generative model for use in a network environment. The server may identify, from a plurality of domains, a domain using the MCP schema in the data file. The server may select, from a plurality of policies corresponding to the plurality of domains, a policy which the MCP schema is to be validated based on the domain. The server may determine that the MCP schema for the MCP is validated in the policy. The server may configure, based on determining that the MCP schema is validated, a data record on a database to indicate an approval of the MCP for use in the network environment.

Abstract: A system or technique can analyze metadata from a first set of images using a neural network to identify classification information about individuals depicted in the images. The system generates data based on this classification information, including attributes of the individuals, and compares this data against predefined criteria for image presentations. If any attributes are found to be non-compliant with the criteria, the system queries a database of second image metadata linked to a set of images. This database includes data generated from prior presentations and classification information provided by individuals depicted in the images. The system locates compliant attributes in the second image metadata, retrieves an image linked to these compliant attributes, and modifies a presentation to include the retrieved image.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.