# SWE-bench Verified — external parity check

This runs a solver over a subset of [SWE-bench Verified](https://www.swebench.com/)
(500 human-validated GitHub issues) through the **official `swebench` Docker
evaluation harness** — the externally-recognized coding-agent benchmark. It
complements the energy/cost frontier in [`../RESULTS.md`](../RESULTS.md): that
shows the cascade's *win* (≈10× cheaper on a realistic op mix); this shows the
cascade is at *parity* on novel multi-file bugs (it escalates them to the same
frontier model via L4, so it neither helps nor hurts resolution there).

## Setup (macOS, Apple Silicon)

```sh
open -a OrbStack                       # a Docker daemon (fast; emulates linux/amd64)
python3.11 -m venv ~/.swebench-venv    # swebench needs Python 3.10+
~/.swebench-venv/bin/pip install swebench datasets
~/.swebench-venv/bin/python swebench/run_swebench.py --solver claude \
    --instances psf__requests-5414 pylint-dev__pylint-7080
```

The SWE-bench prebuilt images are `linux/amd64`; on Apple Silicon they build/run
under emulation (slower, occasionally flaky). 124 GB free disk is plenty for a
small subset; a full 500-task run is heavyweight (per-task images + per-task
agent runs that cost real API budget).

## What it measures, honestly

A run produces resolution rate (the harness builds the task image, applies the
solver's source patch + the hidden test patch, and runs FAIL_TO_PASS +
PASS_TO_PASS) paired with the solver's dollar cost.

**The solver here is a single `claude` invocation per task** — Claude Code
explores + edits + can run the repo's *visible* tests, but it has no access to
the hidden grader tests and no outer test-feedback loop. So this is a **floor**,
not the frontier ceiling: published SWE-bench numbers (80%+) come from
purpose-built multi-turn agentic scaffolds, not one prompt. The number's
absolute value is a property of the scaffold, not the model.

## Sample (9 instances, real Docker eval, aarch64/OrbStack)

```
instance                     resolved   $ cost
psf__requests-1921           ✗ no       $0.299    (wrong fix; broke 24 tests)
psf__requests-5414           ✓ yes      $0.538
psf__requests-6028           (empty)    $0.260    (agent errored, no patch)
pylint-dev__pylint-7080      ✓ yes      $2.893
pallets__flask-5014          ✓ yes      $0.310
psf__requests-1766           ✗ no       $0.425
psf__requests-2931           ✓ yes      $1.327
pylint-dev__pylint-4970      ✗ no       $0.527
pylint-dev__pylint-7277      ✓ yes      $0.759
─────────────────────────────────────────────────────
RESOLVED 5/9 = 56%   total $7.34   $1.47/resolved
```

(Run as two batches via `run_swebench.py`; the second 5-instance batch resolved
3/5 at $1.12/resolved.) Single-invocation Claude Code on a 9-instance fast-repo
subset — a floor, not a tuned agentic scaffold.

## Metered test-time compute (`--rollouts N`)

`--rollouts N` runs N independent rollouts per instance and reports the best-of-k
frontier paired with cumulative dollars — the resolution × $ curve of test-time
compute, **metered** (no other harness measures the cost of the lift). Each k now
reports two resolution numbers:

- **oracle** — resolved if *any* of the first k rollouts resolves. The ceiling of
  any selection method; not realizable (you'd need the hidden grader to know
  which rollout to ship).
- **consensus** — the **no-leak BFT tournament vote** (`consensus_pick`) actually
  selects *one* candidate per instance: the patch the most rollouts agree on
  (plurality over normalized diffs, ties → earliest rollout), never consulting
  the grader. This is the resolution a *realizable* selector achieves. The cost
  is identical to oracle — all k rollouts run either way — so the oracle−consensus
  gap is pure selection-quality signal, not a cost trade.

```
 k   oracle    consensus   cum $
 1     2/3        2/3       2.18
 2     2/3        2/3       4.19
 3     2/3        2/3       6.24
```

Honest reading: this sample's failure was *deterministic*, so even the oracle
ceiling stays flat while cost grows 3× — metering lets you *see* test-time compute
isn't paying off and stop. When rollouts do disagree, consensus is what closes the
oracle gap without peeking at the grader: independent metered workers vote and ship
the agreed patch (this maps directly onto agent-joule's lockstep-quic BFT fleet —
the vote is real consensus, not a heuristic). Realizing the literature's ~7pp lift
further needs **stronger per-rollout agents** (harness engineering —
tools/middleware/memory, e.g. the new `recall` long-term-memory tool in the L3
loop). The selector and its metering are now built; the remaining lever is agent
strength per rollout.

## The harness itself is the deliverable

The harness runs end-to-end with zero errors and full per-test breakdown —
joule-bench can now produce real SWE-bench Verified numbers.
The cascade's resolution on this set **equals the cloud agent's by construction**
(its local tiers miss novel bugs, so it escalates to the same L4 model); running
it separately would just re-invoke the same agent at added cost, so parity here
is established rather than re-measured. The cascade's measurable advantage is the
energy/dollar frontier on the realistic mix, not novel-bug resolution.