Abstract: Systems, methods, and computer-readable media are provided for code analysis. The code analysis system disassembles a particular set of executable code to determine a particular set of translated code and generates a particular translated code vector embedding in an embedding space. The system also generates, for a plurality of sets of source code, a plurality of source code vector embeddings in the embedding space. The system accesses a model trained on training data comprising pairs of translated code and known source code to predict one or more particular sets of source code that are similar to the particular set of translated code based at least in part on vector distances between the plurality of source code vector embeddings and the particular translated code vector embedding. The system causes display of information indicating the one or more predicted sets of source code.

Abstract: A pre-trained context AI agent that was trained using one or more initial training data sets may be accessed. An input data set may be generated that includes a prompt and/or context to request generation of one or more synthetic data elements. A result that includes at least one synthetic data element may be accessed, where the result was generated by a natural language processing technique in response to the input data set. Identification a label for each of the at least one synthetic data element may be facilitated. The context AI agent may be fine-tuned using the label for each of the at least one synthetic data element.