USING A CURRICULUM FOR REINFORCEMENT LEARNING TO TRAIN AN LLM-BASED NETWORK TROUBLESHOOTING AGENT

    公开(公告)号:US20250148291A1

    公开(公告)日:2025-05-08

    申请号:US18388010

    申请日:2023-11-08

    Abstract: In one implementation, a device may determine how well a large language model-based troubleshooting agent for a network was able to perform during a first test having a first difficulty. The device may update the large language model-based troubleshooting agent using reinforcement learning based on how well the large language model-based troubleshooting agent was able to perform during the first test. The device may select a second difficulty for a second test based on how well the large language model-based troubleshooting agent was able to perform during the first test. The device may initiate the second test to assess how well the large language model-based troubleshooting agent is able to perform.

    PREDICTIVE BGP PEERING
    3.
    发明公开

    公开(公告)号:US20240305553A1

    公开(公告)日:2024-09-12

    申请号:US18118769

    申请日:2023-03-08

    CPC classification number: H04L45/04 H04L43/12 H04L45/302

    Abstract: In one embodiment, a device determines a mapping between a network destination and Border Gateway Protocol (BGP) peers located across a plurality of autonomous systems for which the network destination is reachable. The device causes, based on the mapping, performance of probing tests along a plurality of paths to the network destination and via the BGP peers, to obtain path performance measurements for the plurality of paths. The device uses a prediction model to generate predicted performance metrics for the plurality of paths based on the path performance measurements. The device configures, based on the predicted performance metrics for the plurality of paths, the BGP peers with BGP peering policies to convey application traffic associated with the network destination via particular path from among the plurality of paths.

    OBJECTIVE SELECTION FOR LLM-BASED NETWORK TROUBLESHOOTING AND MONITORING AGENTS

    公开(公告)号:US20250148290A1

    公开(公告)日:2025-05-08

    申请号:US18386833

    申请日:2023-11-03

    Abstract: In one implementation, a device receives an input request for a large language model-based troubleshooting agent for a network. The device selects an optimization criterion for the large language model-based troubleshooting agent based on the input request. The device provides the optimization criterion to the large language model-based troubleshooting agent to cause the large language model-based troubleshooting agent to select a particular large language model to process the input request based on the optimization criterion. The device sends, to a user interface, an indication of a result of the particular large language model processing the input request.

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