Firehose

This paper investigates how Large Language Model (LLM) agents can use a file system to store and organize their memories, and whether this approach improves their performance. Practitioners might care because it shows that using a file system as memory can be beneficial for LLM agents, but there are limitations to this approach.

This paper introduces a benchmark to evaluate large language model (LLM) agents on long-horizon office-suite tasks, considering their cost-effectiveness and quality. Practitioners can care about this research because it aims to ensure LLM agents can assist users efficiently and effectively.