# Lab Tools & Systems

> Illustrative sample content for the Lab Assistant RAG template (fictional systems).

## LitMap — graph-based literature discovery
A system that helps researchers map a field rather than just rank hits. Instead of
returning a ranked list like a standard search engine, LitMap is **coverage-oriented**:
it builds a graph over the literature so a user can see the structure of a research area,
discover related work, and navigate connections between papers and ideas. It reflects the
lab's broader thesis that **graph structure is a first-class signal** for understanding
and retrieval.

## ThriftRoute — cutting LLM API cost through routing + distillation
A method/toolkit that reduces the number of expensive large-LLM API calls through
**selective invocation** (only calling the large model when it is actually needed)
combined with **online distillation** (learning from the large model's outputs so a
cheaper model can handle more cases over time). This is the research basis for the
**cost-aware model routing** used inside this very assistant: cheap questions are
answered by a small/fast model and only substantive research questions escalate to a
stronger model.

## How this assistant embodies the lab's research
This lab assistant is itself a small demonstration of several of the lab's lines of work:
- **Retrieval-augmented generation (RAG)** over the lab's publications — see **GraphWeave**.
- **Citation-/graph-aware retrieval**: after vector search, results are expanded one hop
  along a paper graph (shared authors and topics), echoing **GraphWeave** and **CiteTrace**.
- **Trustworthy generation**: answers are grounded in retrieved sources with inline
  citations, and the assistant declines to answer when the corpus does not support a
  claim — reflecting the lab's emphasis on robustness and uncertainty.
- **Cost-aware routing**: a direct nod to **ThriftRoute**.