Abstract: Methods and systems are disclosed for improving the reliability of large language model (LLM) outputs by mapping data from multiple data sources into a unified semantic layer and fine-tuning the LLM based on the semantic layer. An input prompt is processed by the fine-tuned LLM to generate an initial output answer. This output is automatically validated by generating and executing a database query derived from the output answer. The final validated answer is presented when the initial answer and query results match within a predefined threshold, reducing false or hallucinated responses. Practical applications include enhanced cybersecurity monitoring, automated threat investigation, and tenant-specific response generation in multi-tenant environments.

Abstract: A system and method are disclosed for reducing false responses from a large language model. The method includes: mapping a data field from a first source to a semantic layer, the semantic layer including a plurality of data fields; storing data from the first source in a database based on the semantic layer; tokenizing each data field for a first large language model (LLM); fine-tuning the first LLM based on the tokenized semantic layer; providing a prompt to the first LLM, which configures it to generate an output answer; providing the output answer to a second LLM, which configures it to generate a query for the database; executing the query on the database to generate a database output based on the stored data; and providing the output answer in a user interface (UI) in response to determining that the database output and the output answer are within a predefined threshold.

Abstract: A system and method are disclosed for reducing false responses from a large language model. The method includes: mapping a data field from a first source to a semantic layer, the semantic layer including a plurality of data fields; storing data from the first source in a database based on the semantic layer; tokenizing each data field for a first large language model (LLM); fine-tuning the first LLM based on the tokenized semantic layer; providing a prompt to the first LLM, which configures it to generate an output answer; providing the output answer to a second LLM, which configures it to generate a query for the database; executing the query on the database to generate a database output based on the stored data; and providing the output answer in a user interface (UI) in response to determining that the database output and the output answer are within a predefined threshold.

Abstract: A mixed A mixed reality system, comprising: a sensor configured to acquire readings of real-world data, and display, on an output device, a real-world visualization of the real-world data based on the readings to a user, wherein the sensor has one or more parameters affecting the real-world visualization; and a processing circuitry configured to: obtain (a) information of one or more virtual entities located within an area from which the readings are acquired, the information defining, for each of the virtual entities, one or more simulated physical properties, and (b) values of one or more situational parameters indicative of a state of the sensor during acquisition of the readings, wherein the values of the one or more situational parameters are readings of one or more situational sensors, sensing the state of the sensor and its surroundings during acquisition of the readings; determine, for at least one given virtual entity of the virtual entities, a virtual entity visualization of the given virtual entity

Abstract: A mixed A mixed reality system, comprising: a sensor configured to acquire readings of real-world data, and display, on an output device, a real-world visualization of the real-world data based on the readings to a user, wherein the sensor has one or more parameters affecting the real-world visualization; and a processing circuitry configured to: obtain (a) information of one or more virtual entities located within an area from which the readings are acquired, the information defining, for each of the virtual entities, one or more simulated physical properties, and (b) values of one or more situational parameters indicative of a state of the sensor during acquisition of the readings, wherein the values of the one or more situational parameters are readings of one or more situational sensors, sensing the state of the sensor and its surroundings during acquisition of the readings; determine, for at least one given virtual entity of the virtual entities, a virtual entity visualization of the given virtual entity

Abstract: A mixed reality system, comprising: a data acquisition device configured to acquire real-world data; an output device for providing the real-world data to a user; and a processing circuitry configured to: obtain (a) data acquired by the data acquisition device, and (b) information of one or more virtual entities having properties enabling determination of simulated effects of the virtual entities on the data; determine the simulated effects of the virtual entities on the data utilizing the properties; and provide the user with output on the output device being a manipulation of the data reflecting the simulated effects.

Abstract: In some aspects, the disclosure is directed to methods and systems for monitoring and isolating issues over physical layer (layer 1) paths using Flexible Ethernet (FlexE) protocols via client-based operations, administration, and maintenance (OAM) management. Each node within a FlexE communications path may dynamically insert, extract, and/or replace OAM messages from FlexE data streams as needed to ensure that the OAM messages may be provided unimpeded to a destination (e.g. monitoring server or other device), as well as inserting additional monitoring data (e.g. bit error rates, delay measurements, loss measurements, threshold crossing alerts, or other such monitoring notifications or data) for intermediary nodes.

Abstract: A mixed reality system, comprising: a data acquisition device configured to acquire real-world data; an output device for providing the real-world data to a user; and a processing circuitry configured to: obtain (a) data acquired by the data acquisition device, and (b) information of one or more virtual entities having properties enabling determination of simulated effects of the virtual entities on the data; determine the simulated effects of the virtual entities on the data utilizing the properties; and provide the user with output on the output device being a manipulation of the data reflecting the simulated effects.

Abstract: In some aspects, the disclosure is directed to methods and systems for monitoring and isolating issues over physical layer (layer 1) paths using Flexible Ethernet (FlexE) protocols via client-based operations, administration, and maintenance (OAM) management. Each node within a FlexE communications path may dynamically insert, extract, and/or replace OAM messages from FlexE data streams as needed to ensure that the OAM messages may be provided unimpeded to a destination (e.g. monitoring server or other device), as well as inserting additional monitoring data (e.g. bit error rates, delay measurements, loss measurements, threshold crossing alerts, or other such monitoring notifications or data) for intermediary nodes.