Rank benchmark tables by AP and call out always-alert security models (#22)

- Rank benchmark tables by AP and call out always-alert security models (7bc45250a742d659f3f2d2cf9e063a1331102ebc)

README.md

@@ -26,49 +26,51 @@ Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go
 
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.1% | 42.2% | **81.9%** | **55.7%** | **1,525** | 2,087 | **336** | 45.3% | ~$0.210 |
-| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.6% | 43.9% | 43.0% | 43.4% | 800 | 1,023 | 1,061 | 44.9% | $0.239 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **46.8%** | 47.1% | 33.4% | 39.1% | 621 | 698 | 1,240 | **52.4%** | $0.239 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 46.2% | **55.6%** | 5.9% | 10.6% | 109 | **87** | 1,752 | 42.1% | $0.620 |
+| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.6% | 43.9% | 43.0% | 43.4% | 800 | 1,023 | 1,061 | 44.9% | $0.239 |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.1% | 42.2% | **81.9%** | **55.7%** | **1,525** | 2,087 | **336** | 45.3% | ~$0.210 |
 
-_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
+_Sorted by AP. 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](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.7% | 42.4% | **79.5%** | **55.3%** | **337** | 458 | **87** | 47.9% | ~$0.047 |
-| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.5% | 44.0% | 44.3% | 44.2% | 188 | 239 | 236 | 42.4% | $0.054 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **47.9%** | 49.4% | 35.8% | 41.5% | 152 | 156 | 272 | **53.1%** | $0.054 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 47.0% | **51.9%** | 6.6% | 11.7% | 28 | **26** | 396 | 48.1% | $0.138 |
+| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.5% | 44.0% | 44.3% | 44.2% | 188 | 239 | 236 | 42.4% | $0.054 |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.7% | 42.4% | **79.5%** | **55.3%** | **337** | 458 | **87** | 47.9% | ~$0.047 |
 
-_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
+_Sorted by AP. Bold marks the best value in each quality column; lower FP and FN are better._
 
 ## Security
 
 [Public pairs](https://huggingface.co/datasets/perchscan/security-pairs-public)
 
+**Always-alert results:** CLM flags all 3,500 security methods across both splits; SemiF flags 3,499. Their 100% recall comes with 1,960 and 1,959 false alerts respectively, so their F1 scores do not indicate useful security detection.
+
 ### Public — 1,311 pairs
 
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 43.9% | 43.6% | **100.0%** | **60.7%** | **1,142** | 1,479 | **0** | 46.2% | $0.208 |
-| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.9% | 43.6% | **100.0%** | 60.7% | **1,142** | 1,480 | **0** | 45.9% | ~$1.432 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **51.5%** | **53.1%** | 34.6% | 41.9% | 395 | **349** | 747 | 52.6% | $0.507 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 48.2% | 49.7% | 33.1% | 39.7% | 378 | 382 | 764 | **53.7%** | $0.208 |
+| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 43.9% | 43.6% | **100.0%** | **60.7%** | **1,142** | 1,479 | **0** | 46.2% | $0.208 |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 42.9% | 43.6% | **100.0%** | 60.7% | **1,142** | 1,480 | **0** | 45.9% | ~$1.432 |
 
-_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
+_Sorted by AP. 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](https://github.com/TheoLeeCJ/SemIf) | 46.8% | 45.3% | **100.0%** | **62.4%** | **398** | 480 | **0** | 50.0% | $0.074 |
-| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 46.9% | 45.3% | **100.0%** | **62.4%** | **398** | 480 | **0** | 51.3% | ~$0.528 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **49.2%** | 49.4% | 21.9% | 30.3% | 87 | 89 | 311 | **51.5%** | $0.074 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 48.9% | **50.6%** | 20.6% | 29.3% | 82 | **80** | 316 | 40.2% | $0.182 |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 46.9% | 45.3% | **100.0%** | **62.4%** | **398** | 480 | **0** | 51.3% | ~$0.528 |
+| [SemiF](https://github.com/TheoLeeCJ/SemIf) | 46.8% | 45.3% | **100.0%** | **62.4%** | **398** | 480 | **0** | 50.0% | $0.074 |
 
-_Sorted by F1. Bold marks the best value in each quality column; lower FP and FN are better._
+_Sorted by AP. Bold marks the best value in each quality column; lower FP and FN are better._
 
 ## Perch Lint Bench
 
@@ -78,12 +80,12 @@ _Sorted by F1. Bold marks the best value in each quality column; lower FP and FN
 
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 87.0% | 91.1% | **52.8%** | **66.9%** | **113** | 11 | **101** | 86.4% | $0.018 |
 | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **95.0%** | **99.1%** | 50.0% | 66.5% | 107 | **1** | 107 | **96.3%** | — |
+| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 87.0% | 91.1% | **52.8%** | **66.9%** | **113** | 11 | **101** | 86.4% | $0.018 |
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 50.5% | 50.9% | 38.8% | 44.0% | 83 | 80 | 131 | 51.4% | ~$0.010 |
 | [SemiF](https://github.com/TheoLeeCJ/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._
+_Sorted by AP. Bold marks the best value in each quality column; lower FP and FN are better._
 
 ### Private — 71 pairs
 
@@ -94,13 +96,11 @@ _Sorted by F1. Bold marks the best value in each quality column; lower FP and FN
 | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | 51.4% | 50.7% | 50.7% | 50.7% | 36 | 35 | 35 | 56.3% | ~$0.003 |
 | [SemiF](https://github.com/TheoLeeCJ/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._
+_Sorted by AP. 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](https://typesafe.ai/blog/introducing-system-one-models-and-jev). 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.