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cs.LG2024
Real-time Adapting Routing (RAR): Improving Efficiency Through Continuous Learning in Software Powered by Layered Foundation Models
Kirill Vasilevski, Dayi Lin, Ahmed E. Hassan
To balance the quality and inference cost of a Foundation Model (FM, such as large language models (LLMs)) powered software, people often opt to train a routing model that routes r…
cs.AI2024
Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents
Benjamin Rombaut, Sogol Masoumzadeh, Kirill Vasilevski +2
Large language models (LLMs) are increasingly integrated into autonomous systems, giving rise to a new class of software known as Agentware, where LLM-powered agents perform comple…