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pretty_name: Perch Benchmark Results
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Perch benchmark results
This benchmark compares TypeSafe's Jev, a System One classifier, with open models on finding bugs and security issues in individual code methods. Each record contains a method before and after a transaction change, plus input adapted from a Perch scan: method source, imports, callers, callees, and static call-graph edges where available. Models score each version independently, without seeing the repair. Benign reference methods are included, so a before method is not automatically positive.
Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go, Java, C, C++, C#, PHP, and other languages. Bug, security, and user-written semantic lint rules are separate tracks. Public and private splits are disjoint by source repository.
Reading the results
- AP measures how well scores rank actual issues across cutoffs.
- Alert floors: Bug and security checks use their configured Perch floors: 60% for
has_bug, 70% fordoes_what_it_claims, and 60% or 70% for individual CWEs. A method alerts when any applicable issue score is strictly above its floor. Semantic lint uses a 70% run-wide floor. All models use the same floors within each track. - Precision is the share of alerts that are correct; recall is the share of actual issues found; F1 combines both.
- TP, FP, and FN are correct alerts, false alerts, and missed issues.
- Pair order is the share of eligible repairs where the issue-bearing before method scores above the fixed after method. Ties do not count.
Bugs
Public — 2,220 pairs
| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
|---|---|---|---|---|---|---|---|---|---|
| CLM-v0.1-8B | 42.1% | 42.2% | 81.9% | 55.7% | 1,525 | 2,087 | 336 | 45.3% | ~$0.210 |
| SemiF | 44.6% | 43.9% | 43.0% | 43.4% | 800 | 1,023 | 1,061 | 44.9% | $0.239 |
| DiffusionGemma Jev | 46.8% | 47.1% | 33.4% | 39.1% | 621 | 698 | 1,240 | 52.4% | $0.239 |
| Jev | 46.2% | 55.6% | 5.9% | 10.6% | 109 | 87 | 1,752 | 42.1% | $0.620 |
Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better.
Private — 498 pairs
| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
|---|---|---|---|---|---|---|---|---|---|
| CLM-v0.1-8B | 42.7% | 42.4% | 79.5% | 55.3% | 337 | 458 | 87 | 47.9% | ~$0.047 |
| SemiF | 44.5% | 44.0% | 44.3% | 44.2% | 188 | 239 | 236 | 42.4% | $0.054 |
| DiffusionGemma Jev | 47.9% | 49.4% | 35.8% | 41.5% | 152 | 156 | 272 | 53.1% | $0.054 |
| Jev | 47.0% | 51.9% | 6.6% | 11.7% | 28 | 26 | 396 | 48.1% | $0.138 |
Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better.
Security
Public — 1,311 pairs
| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
|---|---|---|---|---|---|---|---|---|---|
| SemiF | 43.9% | 43.6% | 100.0% | 60.7% | 1,142 | 1,479 | 0 | 46.2% | $0.208 |
| CLM-v0.1-8B | 42.9% | 43.6% | 100.0% | 60.7% | 1,142 | 1,480 | 0 | 45.9% | ~$1.432 |
| Jev | 51.5% | 53.1% | 34.6% | 41.9% | 395 | 349 | 747 | 52.6% | $0.507 |
| DiffusionGemma Jev | 48.2% | 49.7% | 33.1% | 39.7% | 378 | 382 | 764 | 53.7% | $0.208 |
Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better.
Private — 439 pairs
| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
|---|---|---|---|---|---|---|---|---|---|
| SemiF | 46.8% | 45.3% | 100.0% | 62.4% | 398 | 480 | 0 | 50.0% | $0.074 |
| CLM-v0.1-8B | 46.9% | 45.3% | 100.0% | 62.4% | 398 | 480 | 0 | 51.3% | ~$0.528 |
| DiffusionGemma Jev | 49.2% | 49.4% | 21.9% | 30.3% | 87 | 89 | 311 | 51.5% | $0.074 |
| Jev | 48.9% | 50.6% | 20.6% | 29.3% | 82 | 80 | 316 | 40.2% | $0.182 |
Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better.
Perch Lint Bench
Public — 214 pairs
| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
|---|---|---|---|---|---|---|---|---|---|
| DiffusionGemma Jev | 87.0% | 91.1% | 52.8% | 66.9% | 113 | 11 | 101 | 86.4% | $0.018 |
| Jev | 95.0% | 99.1% | 50.0% | 66.5% | 107 | 1 | 107 | 96.3% | — |
| CLM-v0.1-8B | 50.5% | 50.9% | 38.8% | 44.0% | 83 | 80 | 131 | 51.4% | ~$0.010 |
| SemiF | 50.0% | 50.7% | 32.2% | 39.4% | 69 | 67 | 145 | 56.5% | $0.018 |
Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better.
Private — 71 pairs
| Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
|---|---|---|---|---|---|---|---|---|---|
| Jev | 94.3% | 97.4% | 53.5% | 69.1% | 38 | 1 | 33 | 94.4% | — |
| DiffusionGemma Jev | 83.0% | 86.4% | 53.5% | 66.1% | 38 | 6 | 33 | 85.9% | $0.006 |
| CLM-v0.1-8B | 51.4% | 50.7% | 50.7% | 50.7% | 36 | 35 | 35 | 56.3% | ~$0.003 |
| SemiF | 48.9% | 48.4% | 42.3% | 45.1% | 30 | 32 | 41 | 50.7% | $0.006 |
Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better.
The bug and security tables use Perch's configured question floors. Semantic lint rules have no configured floor, so this benchmark uses a 70% run-wide floor to limit noisy alerts.
CLM-v0.1-8B still alerts on every security method at these floors; SemiF alerts on all but one public negative method and every private method. Their security F1 is therefore effectively the always-alert baseline, as the FP columns show.
Inference costs cover scoring the method sides in each table. Jev costs are estimated from API-reported input tokens at TypeSafe's published rate. Beam costs are API-reported. CLM cost is an estimated share of a 38.4-minute H100 run costing $2.23 total, allocated by embedding count. Jev's semantic-lint token cost was not recorded. Costs are not ranked.
JevLike (192-byte input), Laya (512-token input), and Kev (packed-branch limit) could not score every full-context method. NanoJev was unavailable on Beam. They are excluded rather than ranked on partial results.
Files and reproduction
results-current.jsonl: current-pack aggregate results for public and private splits; no private pair data.results.jsonl: all recorded result versions, identified by run and protocol ID.predictions/current/: public method probabilities from this run. Private predictions are not published.benchmark.pyandquestion-pack.json: reproducible current-pack scorer and frozen questions.perch-lint-bench-public: paired rules and its runnable scorer.
To run Jev with the current pack, set TYPESAFE_API_KEY or PERCH_API_KEY in your environment:
python3 benchmark.py run --domain bug --scorer jev --predictions bug-jev.jsonl --report bug-jev.json
python3 benchmark.py run --domain security --scorer jev --predictions security-jev.jsonl --report security-jev.json
For another model, use --scorer command --command 'python3 your_adapter.py' --model-revision YOUR_MODEL. The adapter receives the method state and each applicable detection question and returns its true-answer probability. The runner saves predictions for resumption and reports scores only when every method side is complete.