bench/AGENTS.md

# Working guide — Lumbridge Bench

Read `docs/DECISIONS.md` before changing anything structural. It records why
the shape is the shape; the code does not.

## What this repo is

A Lumbridge evidence lab that answers "should we self-host this model?" — quality on our real
work, plus serving performance on our own boxes, in one score card per
`(model × host × quant × serving config × checkpoint)` target.

Built **on** Inspect AI as a dependency. It is not a fork and must not become
one (D1). Adding a benchmark means adding a task, not patching the harness.

## Ground rules

1. **Never commit anything to `data/private/` that was not scrubbed.** No API
   keys, tokens, tailscale hostnames/IPs, customer data, or personal
   information. Samples derived from real tool calls get sanitised at
   authoring time. The content will be sent to third-party APIs (judges,
   baselines) regardless of how private this repo is.

2. **Never publish or export `data/private/`.** Not in the site build, not in
   an artifact, not in a share card, not in a bug report. A public test set is
   a dead test set (D3).

3. **Never present a capped run as a full run.** `--limit` is recorded in the
   result JSON and rendered on the card. Keep it that way.

4. **Do not change `results/` schema fields in place.** Old result files must
   stay readable. Bump `SCHEMA_VERSION` and add fields; never repurpose one.

5. **Results are only comparable across identical serving flags.** Before
   recording a run, confirm the target's flags with `kbench snapshot <host>`.
   If they drifted, update `models.yaml` first — a result carries its own
   snapshot, so a stale registry silently produces incomparable numbers.

## Adding a reference task

Reference tasks come from `inspect_evals` unmodified. Add an entry to
`CATALOG` in `kbench/tasks/__init__.py` with its `primary_metric` (the headline
key in the scorer's metrics) and, if sample scores are dict-valued,
`primary_sample_key`. Do not modify the upstream task.

## Adding a signal task

Signal tasks are ours. Samples live in `data/private/<family>.jsonl`, one JSON
object per line. See `data/README.md` for the format. Use `kbench add` rather
than hand-editing so the canary tag and split assignment are applied.

Assign `split` at authoring time and never move a sample between splits —
`test` samples must never be seen during fine-tuning, or every number after
that point is a lie you cannot detect.

## Perf measurement

`kbench/perf.py` deliberately defeats prefix caching (unique nonce per
request) and fixes output length (`ignore_eos`). If you touch it, preserve
both — without them the numbers look better and mean nothing (D6).

Concurrency-1 numbers describe what a single user feels. Aggregate throughput
at higher concurrency describes what the box can serve. Report both; they can
differ by more than 20x on the Spark.

## Environment

- Runner: EQ (this box). It only needs HTTP to the serving host.
- `your-node` serves vLLM on `:8001`, OpenAI-compatible.
- Inspect addresses it as `openai-api/spark/<model>` with `SPARK_BASE_URL` /
  `SPARK_API_KEY`, both derived from the registry — do not hardcode them.

## Deploy

The standalone site is **retired** (2026-08-01): `bench.karti.ai` permanently redirects to
`lumbridgecorp.com/bench`, and `bench-karti` on web-host :8909 is stopped and disabled. Bench ships as
part of Lumbridge, not as its own destination — see [docs/DEPLOY.md](docs/DEPLOY.md) before standing
any UI back up. Supabase stores auth and the private manual-suggestion inbox in schema `bench`; it is
not an execution queue. The `kbench` CLI runner is unaffected.

Public model suggestions never execute code. The suggestion endpoint may record a reference for owner review, but it must never download, import, schedule, or run a model. D10 supersedes D8 for product behavior; every run begins with explicit owner approval.