Lost in the Middle: How Language Models Use Long Contexts

What it says

Shows that language models often use relevant information less reliably when it is positioned in the middle of long inputs.

Why it matters here

Separates positional long-context failure from cross-session or cross-tenant context bleed.

Editorial caution

Peer review increases confidence in the reported method and result; it does not make every adjacent claim universal.