Lost in the middle, found by structure.
Long-context language models and retrieval pipelines share a documented failure mode: facts buried mid-context get missed. Smarter retrieval slows it. More context makes it worse. For an agent about to act on what it just read, missing a fact is not a slightly weaker answer. It is the wrong action. Limma sidesteps the problem by not asking the model to find the fact at decision time.
Reading is not reasoning.
Researchers have documented a U-shaped attention bias in language models reading long contexts: relevant facts at the very start and very end of the context are retrieved reliably. Facts in the middle are not. The bigger the context, the more pronounced the drop.
Do not ask the model to find the fact at query time.
Every fact is extracted into the workspace at ingest, indexed by entity, metric, and period. Reasoning operates on the workspace, not on the context window. Position in the source has no bearing on retrieval. A fact on page 47 of a 1,500-page binder has the same retrieval cost as a fact on page 4.
Different work, different architecture.
The decision is not which one is better. They are aimed at different work. The same organization will use both. RAG for the parts of the workflow where retrieval is the value. Limma for the parts where provability is the value.
- 01The user is asking a question whose answer is naturally a paragraph
- 02The source material is largely unstructured prose
- 03The user will read the answer and apply their own judgment
- 04Subtle numerical or structural errors are tolerable
- 01The output is a number, a model, a memo, a redline, or anything mechanically checkable
- 02The cost of an undetected error is high (regulatory, financial, legal)
- 03The work spans multiple sources that must be reconciled with each other
- 04A human downstream will defend the output to an auditor, regulator, or counterparty
Stop asking the model to find the fact.
For agents that act on what they just read, missing a mid-context fact is not a slightly weaker answer. It is the wrong action. Bring the corpus, the agent, and the action. We will show you what the verification step catches that the long-context read does not.