# Lumbridge Compute evaluations (`lumbridge/v1`)

Lumbridge Compute evaluates the model configuration that is actually serving: weights,
quantization, context, runtime, parsers, and speculative decoder. A model name
without its serving configuration is not a reproducible benchmark target.

```bash
lumbridge-compute eval ls
lumbridge-compute eval run smoke
lumbridge-compute eval run performance --model brain --repeat 5
lumbridge-compute eval run finance-core --base-url http://your-node:8001/v1
```

Suites live in `evals/*.eval.yaml`. Each case has a stable id, prompt, category,
generation limit, and deterministic assertions. Runs produce append-only JSON in
`eval-results/` with raw outputs and per-sample metrics.

## Metrics

- **TTFT**: wall time until the first streamed content or reasoning token.
- **Prefill tok/s (approximate)**: API-reported prompt tokens divided by TTFT.
  This is client-observed and includes queueing/scheduling; server-native prefill
  metrics should be added as a separate source rather than conflated with it.
- **Decode tok/s**: completion tokens divided by time after the first token.
- **Score**: share of samples satisfying every declared assertion.

Performance runs should include warmups in automation and record hardware, Lumbridge Compute
scene, runtime version, model revision, and cold/warm cache state. The v1 artifact
is deliberately local and portable; a future registry can ingest the same JSON.

## Boundary with Bench, Arena, and Forge

Compute's native suites are post-activation health and performance checks for the exact
configuration serving on a node. They do not grow into another training harness. Bench owns
longitudinal model evidence, Arena owns task distributions and rewards on upstream Prime
Intellect Verifiers, and Forge records any Prime-RL handoff and returned training artifact.

Compute resumes ownership only after an approved checkpoint has been returned and verified:
local transfer, footprint admission, registry promotion, scene scheduling, and serving-process
lifecycle. The training framework owns optimization and distributed execution.