> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rafflesia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Evals

> Deterministic environments that grade research agents and the search engine against hidden references and curated ground truth.

Two evaluation systems measure whether the engine — and the agents built on it —
actually work. Both grade against evidence the agent cannot see, so scores can't
be gamed.

## Structural-biology environments

Each task is a resettable workspace: an agent gets input files and a prompt, and
must produce named artifacts — a structure model, a contact table, a rendered
image. Those artifacts are graded deterministically against a hidden reference,
and the reward is a weighted composite of geometric measurements (lDDT, TM-score,
DockQ, clash, contact F1, map fit) versus a per-task threshold. Held-out
validation metrics penalize overfitting to the evidence the agent can see. Tool
use is policy-gated rather than scored, and full trajectories are logged for
replay and audit.

## Homology-recall benchmarks

For the search engine itself, curated **truth sets** derived from SCOPe/CATH
structure classification and Pfam/InterPro domain families label query–target
pairs as positive, negative, or ignore. Runs are scored on biological recall
(`positive_recall_at_k`) against false-positive ceilings, across strata like
remote homologs, low-complexity, transmembrane, and shuffled decoys. Tool
outputs (MMseqs2, DIAMOND, BLAST) are kept as comparison baselines only — never
as ground-truth labels.