Yadnesh Salvi

Story · Semantic Scholar

Token Write Amplification in LLM-Mediated State Stores (Semantic Scholar)

paper · Story page

A cutaway diagram: one card enters a machine at the left and a crowd of copies circulates inside along looping arrows before a single card exits at the right. A smaller diagram below shows three cards bundled together before entering, with far fewer copies circulating.

Keeping a persistent memory store costs an average of 8.00 write-path LLM tokens per raw context token, across five store configurations and seven agent workloads. A buffer that groups related writes brings the ratio to 3.02 without touching the backend.

In plain words

  • Researchers found substantial repeated work in the way artificial intelligence systems save information for later use.
  • When saving new information, these systems often process ideas they already have stored.
  • The researchers reduced this repeated work by grouping related memory updates before saving them.
  • For teams maintaining these memories, the tested approach reduced processing without requiring changes to their existing storage systems.

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