Abstract: The invention is related to a system (1) for analyzing an image of a real world scene, the system (1) comprising image providing means (13) for obtaining the image of the real world scene, a large multimodal model LMM module (11) providing a large language model LLM functionality and a visual language model VLM functionality, wherein the LMM module (11) is configured to analyze the image using its VLM functionality for generating a first scene description of the received image, a structured memory (12) for storing real world information, wherein the structured memory (12) is connected to the LMM module (11) and configured to generate a second scene description of the received image based on the stored real world information and to 10 provide the description to the LMM module (11), wherein the LMM module (11) is configured to identify differences between the first scene description and the second scene description and to generate an enhanced scene description based on the identified mismatches.

Abstract: The disclosure concerns a method for generating a response to a query from a user or an agent using a vector embedding memory storing vectors and generated chunks of natural language text, and a graph-based memory storing information in the form of nodes interconnected by links. The method retrieves matching vectors from the vector embedding memory based on a semantic proximity of the stored vectors in the vector embedding memory with vectors generated based on the chunks of natural language text of the obtained query, determines and activates nodes stored in the graph-based memory based on a correspondence with the matching vectors and connected nodes based on links between the activated nodes, generates a response to the query based on chunks of text determined based on the semantic proximity, and based on determined chunks of text that correspond to the activated nodes as additional background information.