Abstract: Various methods and processes, apparatuses/systems, and media for automated structure retrieval of complex standard operating procedures (SOPs) are disclosed. A processor encodes the SOP as a Directed Acyclic Graph (DAG) to keep track of the dependencies between SOP tasks or steps and retain the sequential aspect of the SOP execution. To be able to support long SOPs, the processor implements a multi-phase approach where the processor first performs a “segmentation” step to break the SOP into task segments which are then transformed into a DAG by calling a structure generation module. The segmentation is utilized recursively to attain a fined-grain decomposition of the SOP to facilitate effective DAG generation.

Abstract: A method and a system for using a large language model (LLM) to automatically translate textual instructions into executable software code via skill distillation and composition are provided. The method includes: receiving a request for performing a task and a prompt; providing, as an input to an LLM, the first request and a response to the prompt; receiving, from the LLM, a set of code that implements a function that corresponds to a skill that is usable for performing the task; generating a test that relates to the task; performing the test by executing the set of code and checking whether the task has been successfully completed; and when the task has been successfully completed, storing the set of code in a skills library. Sets of code stored in the skills library may then be accessed and combined in order to perform larger tasks.

Abstract: A method and a system for obtaining an evaluation of a quality of software code that is generated by using a large language model (LLM) are provided. The method includes: receiving a set of instructions for performing a task and generating an output; providing, as an input to an LLM, a list of available application programming interfaces (APIs) and the instructions, together with a submission of a request to the LLM to select one API and to generate a set of executable code based on the instructions; receiving, from the LLM, a selection of one API and the set of executable code; executing the set of executable code in order to perform the first task and generate the output; and evaluating an accuracy, a robustness, and/or a consistency of the set of executable code.

Abstract: A method for providing a synthetic neural data model is disclosed. The method includes generating a model that simulates an electronic communication network; appending agents to the model, the agents relating to a software component that sends orders to the model based on a predetermined timestep; assigning a fixed grid to the model, the fixed grid including a tick size that relates to a fixed granularity; calibrating each of the agents by using a calibration data set; inputting the model and historical book data to a neural network; and training, via the neural network, a neural network extension of the model by using an optimizer.