Abstract: Aspects of the subject disclosure may include, for example, identifying source equipment is associated with a group of recipient equipment based on historical financial data, and determining a processing latency between the source equipment and each recipient equipment of the group of recipient equipment based on the historical financial data. Further embodiments can include determining a processing rate for each processing latency, and determining a verification latency to perform security authentication for each recipient equipment. Additional embodiments can include determining a verification rate for each verification latency, determining a service metric based on the processing rates and the verification rates, the service metric being associated with a token service that expediates a transfer of financial data between the source equipment and the group of recipient equipment, and presenting the service metric at the source equipment to enroll in the token service. Other embodiments are disclosed.

Abstract: A system is presented that optimally integrates a plurality of distinct artificial intelligence and machine learning (AI/ML) models into a cohesive AI/ML paradigm that improves an overall quality and scope of the plurality of distinct AI/ML models. The system may be configured to receive an input via an interface of at least one processor; instantiate a tokenized output of the at least one processor; establish, with the at least one processor, a plurality of connections that respectively correspond to and communicate with each AI/ML model from among the plurality of distinct AI/ML models; perform, by the at least one processor, an iterative sequence that utilizes the plurality of connections to populate the tokenized output based on the input; and transmit the tokenized output to the interface.

Abstract: A method and system for generating a response to a user prompt may be provided. The method may include obtaining the user prompt comprising the at least one objective and the at least one user preference dimension. The method may include performing an inference of the user prompt via the combination ML model, the combination ML model may include a base large language model (LLM) and at least one autoregressive reward model (ARM). The method may include generating the response corresponding to the at least one objective correlating with the at least one user preference dimension via the combination ML model.

Abstract: Aspects of the subject disclosure may include, for example, identifying source equipment is associated with a group of recipient equipment based on historical financial data, and determining a processing latency between the source equipment and each recipient equipment of the group of recipient equipment based on the historical financial data. Further embodiments can include determining a processing rate for each processing latency, and determining a verification latency to perform security authentication for each recipient equipment. Additional embodiments can include determining a verification rate for each verification latency, determining a service metric based on the processing rates and the verification rates, the service metric being associated with a token service that expediates a transfer of financial data between the source equipment and the group of recipient equipment, and presenting the service metric at the source equipment to enroll in the token service. Other embodiments are disclosed.

Abstract: A method and system for performing information-directed pessimism in offline learning for reduction of distribution mismatch are disclosed. The method includes determining a first and second distribution for the ML model based on different datasets, and determining a presence of a distribution mismatch between the first distribution and the second distribution. The method further includes calculating a value for an individual state-action pair in a training dataset and comparing the calculated value against a reference data distribution, determining a difference between the calculated value and the reference data distribution and comparing the difference against a reference threshold.

Abstract: A method and system for accounting for missing not-at-random (MNAR) data in training dataset via Bayesian regularization are disclosed. The method includes acquiring historical data of an organization, the historical data including the MNAR data. The method further includes performing estimation for two of at least three unknown quantities based on the historical data, and injecting quantitative information for remaining one of the at least three unknown quantities based on qualitative information regarding nature of missingness. Lastly, the method reassembles the estimation for two of the at least three unknown quantities and injected quantitative information for the remaining one of the at least three unknown quantities, to provide a modified function.