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run_swebench.py runs N independent rollouts per instance and reports the best-of-k frontier: resolution within the first k rollouts vs cumulative dollars. First best-of-3 sample stayed flat at 2/3 while cost grew 3x — the failure was deterministic, so even oracle selection (the upper bound) cannot lift it. That is the value of metering: it shows when test-time compute is not paying off. The literature's ~7pp lift needs stronger per-rollout agents + summarize-then-vote selection, not naive best-of-N; documented as the next builds. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
91 lines
4.4 KiB
Markdown
91 lines
4.4 KiB
Markdown
# SWE-bench Verified — external parity check
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This runs a solver over a subset of [SWE-bench Verified](https://www.swebench.com/)
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(500 human-validated GitHub issues) through the **official `swebench` Docker
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evaluation harness** — the externally-recognized coding-agent benchmark. It
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complements the energy/cost frontier in [`../RESULTS.md`](../RESULTS.md): that
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shows the cascade's *win* (≈10× cheaper on a realistic op mix); this shows the
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cascade is at *parity* on novel multi-file bugs (it escalates them to the same
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frontier model via L4, so it neither helps nor hurts resolution there).
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## Setup (macOS, Apple Silicon)
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```sh
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open -a OrbStack # a Docker daemon (fast; emulates linux/amd64)
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python3.11 -m venv ~/.swebench-venv # swebench needs Python 3.10+
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~/.swebench-venv/bin/pip install swebench datasets
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~/.swebench-venv/bin/python swebench/run_swebench.py --solver claude \
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--instances psf__requests-5414 pylint-dev__pylint-7080
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```
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The SWE-bench prebuilt images are `linux/amd64`; on Apple Silicon they build/run
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under emulation (slower, occasionally flaky). 124 GB free disk is plenty for a
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small subset; a full 500-task run is heavyweight (per-task images + per-task
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agent runs that cost real API budget).
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## What it measures, honestly
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A run produces resolution rate (the harness builds the task image, applies the
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solver's source patch + the hidden test patch, and runs FAIL_TO_PASS +
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PASS_TO_PASS) paired with the solver's dollar cost.
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**The solver here is a single `claude` invocation per task** — Claude Code
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explores + edits + can run the repo's *visible* tests, but it has no access to
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the hidden grader tests and no outer test-feedback loop. So this is a **floor**,
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not the frontier ceiling: published SWE-bench numbers (80%+) come from
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purpose-built multi-turn agentic scaffolds, not one prompt. The number's
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absolute value is a property of the scaffold, not the model.
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## Sample (9 instances, real Docker eval, aarch64/OrbStack)
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```
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instance resolved $ cost
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psf__requests-1921 ✗ no $0.299 (wrong fix; broke 24 tests)
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psf__requests-5414 ✓ yes $0.538
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psf__requests-6028 (empty) $0.260 (agent errored, no patch)
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pylint-dev__pylint-7080 ✓ yes $2.893
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pallets__flask-5014 ✓ yes $0.310
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psf__requests-1766 ✗ no $0.425
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psf__requests-2931 ✓ yes $1.327
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pylint-dev__pylint-4970 ✗ no $0.527
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pylint-dev__pylint-7277 ✓ yes $0.759
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─────────────────────────────────────────────────────
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RESOLVED 5/9 = 56% total $7.34 $1.47/resolved
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```
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(Run as two batches via `run_swebench.py`; the second 5-instance batch resolved
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3/5 at $1.12/resolved.) Single-invocation Claude Code on a 9-instance fast-repo
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subset — a floor, not a tuned agentic scaffold.
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## Metered test-time compute (`--rollouts N`)
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`--rollouts N` runs N independent rollouts per instance and reports the best-of-k
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frontier: resolution within the first k rollouts vs the cumulative dollars they
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cost — the resolution × $ curve of test-time compute, **metered** (no other
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harness measures the cost of the lift). First best-of-3 sample (3 `requests`
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instances):
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```
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k resolved resol.% cum $
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1 2/3 67% 2.18
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2 2/3 67% 4.19
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3 2/3 67% 6.24
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```
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Honest reading: this sample's failure was *deterministic*, so even oracle
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selection (resolved-if-any — the upper bound of any selection method) stays flat
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while cost grows 3×. That is the value of metering: you can *see* test-time
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compute isn't paying off and stop. Realizing the literature's ~7pp lift needs
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**stronger per-rollout agents** (harness engineering — tools/middleware/memory)
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and **summarize-then-tournament-vote selection**, not naive best-of-N. Those are
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the next builds.
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## The harness itself is the deliverable
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The harness runs end-to-end with zero errors and full per-test breakdown —
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joule-bench can now produce real SWE-bench Verified numbers.
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The cascade's resolution on this set **equals the cloud agent's by construction**
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(its local tiers miss novel bugs, so it escalates to the same L4 model); running
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it separately would just re-invoke the same agent at added cost, so parity here
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is established rather than re-measured. The cascade's measurable advantage is the
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energy/dollar frontier on the realistic mix, not novel-bug resolution.
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