Link model cards and explain result columns

README.md

@@ -5,7 +5,9 @@ pretty_name: Perch Benchmark Results
 
 # Perch benchmark results
 
-Bug and security results are separate. The primary tables compare models given the same saved Perch method context and one broad question per method (`perch-broad-v1`). Average precision (AP) measures ranking across all scores; precision, recall, and F1 use an uncalibrated 0.5 cutoff. Pair order is the share of eligible repairs where the before method scored above the after method.
+Bug and security results are separate. The primary tables compare models given the same saved Perch method context and one broad question per method (`perch-broad-v1`). Model names link to their model cards; Jev links to TypeSafe's description because it has no public model card.
+
+**Columns:** AP (average precision) summarizes how well the model ranks issue-bearing methods above non-issue methods across score cutoffs. At the 0.5 cutoff, a method is flagged when its issue score is at least 50%. Precision is the share of flagged methods that have the issue; recall is the share of issue-bearing methods flagged; F1 is their harmonic mean. TP is a correctly flagged method, FP is a method falsely flagged, and FN is an issue-bearing method missed. Pair order is the share of eligible before/after pairs where the before method scores higher than the fixed after method; ties do not count as correct.
 
 The public pairs and their per-method predictions are available here. Private pair-level data and predictions are not published. The public and private sets are disjoint by source repository across both domains.
 
@@ -17,17 +19,17 @@ The public pairs and their per-method predictions are available here. Private pa
 
 | Model | AP | Precision at 0.5 | Recall at 0.5 | F1 at 0.5 | TP | FP | FN | Pair order |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| Hosted Jev 1.13.0 | 47.6% | 49.9% | 22.0% | 30.6% | 410 | 412 | 1451 | 42.5% |
-| Released JevK5 | 44.1% | 47.5% | 7.6% | 13.1% | 141 | 156 | 1720 | 32.1% |
-| Released Gemma 4 E4B + Jev-lite | 43.7% | 44.8% | 2.3% | 4.4% | 43 | 53 | 1818 | 22.4% |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 47.6% | 49.9% | 22.0% | 30.6% | 410 | 412 | 1451 | 42.5% |
+| [JevK5](https://huggingface.co/alibiserikbay/JevK5) | 44.1% | 47.5% | 7.6% | 13.1% | 141 | 156 | 1720 | 32.1% |
+| [jev-lite](https://huggingface.co/vagmi/jev-lite) | 43.7% | 44.8% | 2.3% | 4.4% | 43 | 53 | 1818 | 22.4% |
 
 ### Private — 498 pairs
 
 | Model | AP | Precision at 0.5 | Recall at 0.5 | F1 at 0.5 | TP | FP | FN | Pair order |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| Hosted Jev 1.13.0 | 48.6% | 50.7% | 24.1% | 32.6% | 102 | 99 | 322 | 43.4% |
-| Released JevK5 | 45.8% | 46.4% | 9.2% | 15.4% | 39 | 45 | 385 | 32.7% |
-| Released Gemma 4 E4B + Jev-lite | 45.2% | 60.0% | 3.5% | 6.7% | 15 | 10 | 409 | 22.7% |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 48.6% | 50.7% | 24.1% | 32.6% | 102 | 99 | 322 | 43.4% |
+| [JevK5](https://huggingface.co/alibiserikbay/JevK5) | 45.8% | 46.4% | 9.2% | 15.4% | 39 | 45 | 385 | 32.7% |
+| [jev-lite](https://huggingface.co/vagmi/jev-lite) | 45.2% | 60.0% | 3.5% | 6.7% | 15 | 10 | 409 | 22.7% |
 
 ## Security
 
@@ -37,30 +39,30 @@ The public pairs and their per-method predictions are available here. Private pa
 
 | Model | AP | Precision at 0.5 | Recall at 0.5 | F1 at 0.5 | TP | FP | FN | Pair order |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| Hosted Jev 1.13.0 | 49.2% | 54.4% | 10.2% | 17.2% | 117 | 98 | 1025 | 40.5% |
-| Released JevK5 | 44.8% | 46.4% | 12.4% | 19.6% | 142 | 164 | 1000 | 30.6% |
-| Released Gemma 4 E4B + Jev-lite | 43.3% | 36.0% | 3.2% | 5.8% | 36 | 64 | 1106 | 22.9% |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 49.2% | 54.4% | 10.2% | 17.2% | 117 | 98 | 1025 | 40.5% |
+| [JevK5](https://huggingface.co/alibiserikbay/JevK5) | 44.8% | 46.4% | 12.4% | 19.6% | 142 | 164 | 1000 | 30.6% |
+| [jev-lite](https://huggingface.co/vagmi/jev-lite) | 43.3% | 36.0% | 3.2% | 5.8% | 36 | 64 | 1106 | 22.9% |
 
 ### Private — 439 pairs
 
 | Model | AP | Precision at 0.5 | Recall at 0.5 | F1 at 0.5 | TP | FP | FN | Pair order |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| Hosted Jev 1.13.0 | 48.9% | 52.6% | 2.5% | 4.8% | 10 | 9 | 388 | 36.2% |
-| Released JevK5 | 47.4% | 45.0% | 4.5% | 8.2% | 18 | 22 | 380 | 37.9% |
-| Released Gemma 4 E4B + Jev-lite | 47.3% | 43.8% | 1.8% | 3.4% | 7 | 9 | 391 | 29.4% |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | 48.9% | 52.6% | 2.5% | 4.8% | 10 | 9 | 388 | 36.2% |
+| [JevK5](https://huggingface.co/alibiserikbay/JevK5) | 47.4% | 45.0% | 4.5% | 8.2% | 18 | 22 | 380 | 37.9% |
+| [jev-lite](https://huggingface.co/vagmi/jev-lite) | 47.3% | 43.8% | 1.8% | 3.4% | 7 | 9 | 391 | 29.4% |
 
 ## Different input protocols
 
-These released models received their native decision inputs, so their scores are reported separately from the prompt-matched tables above.
+These models received their native decision inputs, so their scores are reported separately from the prompt-matched tables above.
 
 | Domain | Track | Model | Protocol | AP | F1 at 0.5 | TP | FP | FN |
 | --- | --- | --- | --- | ---: | ---: | ---: | ---: | ---: |
-| Bug | Public | Released CLM-v0.1-8B | `clm-native-choice-6144-v1` | 44.0% | 21.2% | 258 | 317 | 1603 |
-| Bug | Private | Released CLM-v0.1-8B | `clm-native-choice-6144-v1` | 45.2% | 25.1% | 74 | 92 | 350 |
-| Security | Public | Released CLM-v0.1-8B | `clm-native-choice-6144-v1` | 44.0% | 52.1% | 741 | 959 | 401 |
-| Security | Public | Released SecureBERT2.0 | `perch-security-state-4096-v1` | 43.6% | 60.6% | 1140 | 1479 | 2 |
-| Security | Private | Released CLM-v0.1-8B | `clm-native-choice-6144-v1` | 47.1% | 55.4% | 270 | 306 | 128 |
-| Security | Private | Released SecureBERT2.0 | `perch-security-state-4096-v1` | 43.0% | 62.4% | 397 | 478 | 1 |
+| Bug | Public | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | `clm-native-choice-6144-v1` | 44.0% | 21.2% | 258 | 317 | 1603 |
+| Bug | Private | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | `clm-native-choice-6144-v1` | 45.2% | 25.1% | 74 | 92 | 350 |
+| Security | Public | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | `clm-native-choice-6144-v1` | 44.0% | 52.1% | 741 | 959 | 401 |
+| Security | Public | [SecureBERT2.0](https://huggingface.co/cisco-ai/SecureBERT2.0-code-vuln-detection) | `perch-security-state-4096-v1` | 43.6% | 60.6% | 1140 | 1479 | 2 |
+| Security | Private | [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | `clm-native-choice-6144-v1` | 47.1% | 55.4% | 270 | 306 | 128 |
+| Security | Private | [SecureBERT2.0](https://huggingface.co/cisco-ai/SecureBERT2.0-code-vuln-detection) | `perch-security-state-4096-v1` | 43.0% | 62.4% | 397 | 478 | 1 |
 
 At 0.5, SecureBERT2.0 flags nearly every security method. Its high F1 therefore does not indicate useful alerting precision.
 
@@ -70,11 +72,11 @@ Each before and after method was scored independently without showing the repair
 
 | Model | Frozen checkpoint |
 | --- | --- |
-| Hosted Jev | Service version 1.13.0; provider weights not public |
-| JevK5 | `alibiserikbay/JevK5@3c673298eb7f2dc7cb98019262c55b1fac01a0bc` |
-| Gemma 4 E4B + Jev-lite | Base `ee0ef6023621cff504d758262d4e04895a5af4a2`; released adapter `90b23bc26c53f5a897684456893b2b7edeb90297` |
-| CLM-v0.1-8B | Encoder `b968826d9c46dd6066d109eabc6255188de91218`; released head SHA-256 `b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5` |
-| SecureBERT2.0 | Released `cisco-ai/SecureBERT2.0-code-vuln-detection`; exact snapshot revision was not recorded |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | Service version 1.13.0; provider weights not public |
+| [JevK5](https://huggingface.co/alibiserikbay/JevK5) | `alibiserikbay/JevK5@3c673298eb7f2dc7cb98019262c55b1fac01a0bc` |
+| [jev-lite](https://huggingface.co/vagmi/jev-lite) | Gemma base `ee0ef6023621cff504d758262d4e04895a5af4a2`; adapter `90b23bc26c53f5a897684456893b2b7edeb90297` |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | Encoder `b968826d9c46dd6066d109eabc6255188de91218`; head SHA-256 `b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5` |
+| [SecureBERT2.0](https://huggingface.co/cisco-ai/SecureBERT2.0-code-vuln-detection) | `cisco-ai/SecureBERT2.0-code-vuln-detection`; exact snapshot revision was not recorded |
 
 The collection is enriched for before/after changes, so its positive rate and precision do not estimate production-scan prevalence. Some security labels are inferred from advisory fixes rather than independently confirmed for every method.