Datasets:
Swap the agent harness, keep the model: one bug in twelve moves — and it moves on persistence, not reasoning
Rig: one RTX 5090 32GB · llama.cpp · Qwen3.6-27B-Q6_K @ :8090 · think-off · temp 0
Question: how much of an agentic-coding score is the model and how much is the scaffold wrapped around it? Hold the model fixed, change only the harness, grade on real bugs.
Setup
The same local model (Qwen3.6-27B-Q6_K, served by one llama-server on :8090) drove two different agent harnesses against the same 12 SWE-bench Verified bugs, graded by the official SWE-bench harness (apply the patch, run FAIL_TO_PASS + PASS_TO_PASS):
- rig-native loop — our own OpenAI tool-calling agent (
read/list/search/edit/run-bashviadocker exec), budget 40 tool-calling steps per bug. - omp v16.1.14 — a dependency-free third-party CLI coding agent, run headless (
omp -p --auto-approve) inside each bug's container, pointed at the same:8090endpoint via itsmodels.yml. Budget 450s wall-clock per bug.
Same weights, same context window, same decode settings, same bugs. The only thing that changes is the loop around the model: its prompt strategy, its tool wiring, and — by construction — its stopping rule.
Result: a strict superset, +1
| harness | resolved | the delta |
|---|---|---|
| rig-native (40-step) | 8/12 | — |
| omp v16.1.14 (450s) | 9/12 | +sphinx-8621 |
omp resolves a strict superset: every bug the native loop fixed, plus one — sphinx-doc__sphinx-8621. Nothing is traded away. And both harnesses miss the same hard bugs.
The per-bug grid is the whole story
| bug | rig-native | omp |
|---|---|---|
| astropy-12907, django-16082, matplotlib-23314, flask-5014, xarray-3677, pytest-6202, scikit-learn-14141, sympy-22914 | ✅ resolved | ✅ resolved |
| sphinx-8621 | gave up (empty patch) | ✅ resolved |
| pylint-7080 | gave up (empty patch) | wrong patch |
| requests-1921 | wrong patch | wrong patch |
| seaborn-3187 | gave up (empty patch) | gave up (empty patch) |
Eight bugs the model resolves no matter which harness drives it. Three hard bugs neither harness resolves under either scaffold. One bug in the middle flips — and how it flips is the finding.
The mechanism: the scaffold buys persistence, not intelligence
Look at the miss types, not the counts. On the four hard bugs, the native loop produced 3 empty patches (it explored, then terminated without committing any edit) and 1 wrong patch. omp produced 1 empty patch and 3 attempts:
sphinx-8621: native gave up → omp committed a patch that passed. The resolve.pylint-7080: native gave up → omp committed a patch that failed. Same persistence, unlucky bug.requests-1921: both committed wrong patches.seaborn-3187: both gave up.
The scaffold didn't make the model smarter — both harnesses hit the same ceiling on the same bugs (seaborn, requests, pylint resolve under neither). What omp's loop changed is the give-up rate: it keeps the model working long enough to commit a patch where the native loop quits empty-handed. On these four bugs, empty patches fell from 3 → 1. One of those converted give-ups happened to land a passing fix. That is the entire +1/12.
This is the rig's recurring tell, seen from the other side: across the reality-anchor work, empty-patch rate is the give-up fingerprint that separates models. Here it separates harnesses on a fixed model — and the lever it pulls is persistence-under-ambiguity, the exact axis a synthetic tool-fluency score never measures.
Honest caveats
- n = 12, single seed. +1/12 is inside the noise band. The claim is not "omp is 12% better" — it's the direction plus the mechanism: same ceiling, fewer give-ups, a strict superset. A single-seed +1 is a signpost, not a measurement.
- The budgets differ by construction (40 steps vs 450s). That is not a confound to apologize for — it is the scaffold variable. "Harness" means prompt strategy + tool wiring + stopping policy, bundled; the stopping policy is exactly where the persistence delta lives. Equalizing them would be measuring a different, dis-assembled thing.
- The native baseline is the 12-bug subset of the rig's 30-bug run (Qwen3.6-27B, 19/30). SWE-bench grades each instance independently in its own container, so the subset is identical to having run those 12 alone.
- Bounded scout A/B. Base model, 12 bugs, one scaffold pair. The full version — a coding-tuned model, 30 bugs, a third harness (Hermes) — is the >3h follow-on, parked under the rig's task-time cap.
Verdict
On a fixed local model, the agent harness is a secondary lever: it moved one bug in twelve, and it did so by quitting less, not by reasoning better. Worth swapping in a more persistent scaffold if your model already clears the easy bugs and your losses are give-ups (empty patches) rather than wrong fixes — that's the regime omp helps. Not worth it if you're hoping the harness will crack bugs the model fundamentally can't: both loops died on the same three. The model is the ceiling; the scaffold decides how often you stop short of it.
Generation: rig-native = lib/agentic/native/{tools_repo,run_swebench}.py (40-step loop, temp 0) in notwitcheer/llm-bench-rig; omp = omp v16.1.14, headless omp -p in each container, same :8090 model via models.yml, 450s/bug. Grading = official swebench harness. Both report JSONs and the per-bug grid: reports/omp-harness-as-variable.png.