Update Jev and Liquid bug benchmark results (#25)

- Update Jev and Liquid bug benchmark results (c636ba3c324d4d2d3980ee364b88f1e477f8bbd5)
- Keep benchmark card neutral (e5675abe06fb7479cb45cdefb1185dc908b8dda1)

README.md

@@ -25,7 +25,7 @@ Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go
 ## 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% for `does_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.
+- **Alert floors:** A method alerts when any applicable issue score is strictly above the floor recorded for its run. Semantic lint uses a 70% run-wide floor. Per-row question packs and protocols are recorded in `results-current.jsonl`.
 - **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.
@@ -34,15 +34,13 @@ Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go
 
 [Public pairs](https://huggingface.co/datasets/perchscan/bug-pairs-public)
 
-**Liquid d1 at the configured bug floors:** 26 of 4,440 public methods raised alerts, finding 13 of 1,861 labelled bugs.
-
 ### Public — 2,220 pairs
 
 | Model | AP | Precision | Recall | F1 | TP | FP | FN | Pair order | Inference cost |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | **46.8%** | 47.1% | 33.4% | 39.1% | 621 | 698 | 1,240 | **52.4%** | $0.239 |
-| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | **46.8%** | 50.0% | 0.7% | 1.4% | 13 | **13** | 1,848 | 49.2% | Free tier |
-| [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 |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **47.3%** | 50.1% | 17.7% | 26.1% | 329 | 328 | 1,532 | 43.8% | $0.551 |
+| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 46.8% | 47.1% | 33.4% | 39.1% | 621 | 698 | 1,240 | **52.4%** | $0.239 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 46.6% | **53.9%** | 4.1% | 7.6% | 76 | **65** | 1,785 | 50.1% | Free tier |
 | [SemiF](https://github.com/TheoLeeCJ/SemIf) | 44.6% | 43.9% | 43.0% | 43.4% | 800 | 1,023 | 1,061 | 44.9% | $0.239 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 43.8% | 44.7% | 25.3% | 32.3% | 471 | 583 | 1,390 | 45.0% | $0.182 |
 | [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 |
@@ -53,9 +51,9 @@ _Sorted by AP. 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) | **47.9%** | 49.4% | 35.8% | 41.5% | 152 | 156 | 272 | **53.1%** | $0.054 |
-| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | 47.2% | **75.0%** | 1.4% | 2.8% | 6 | **2** | 418 | 48.1% | Free tier |
-| [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 |
+| [Liquid d1](https://docs.liquid.ai/lfm/models/decision-models) | **48.7%** | **56.4%** | 5.2% | 9.5% | 22 | **17** | 402 | 49.6% | Free tier |
+| [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) | 47.1% | 48.9% | 20.0% | 28.4% | 85 | 89 | 339 | 42.9% | $0.123 |
 | [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 45.5% | 46.5% | 28.5% | 35.4% | 121 | 139 | 303 | 44.6% | $0.040 |
 | [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 |
@@ -96,7 +94,7 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 
 ## Perch Lint Bench
 
-![Public Perch Lint Bench average precision, with Jev and Liquid d1 in their brand colors](assets/perch-lint-bench-public.png)
+![Average precision on public semantic lint pairs](assets/perch-lint-bench-public.png)
 
 [Public pairs](https://huggingface.co/datasets/perchscan/perch-lint-bench-public)
 
@@ -127,7 +125,7 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 _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.
+Bug and security runs use the question floors recorded with each result. Semantic lint rules use a 70% run-wide floor.
 
 Span-01 uses its [behavior-scoring API](https://www.respan.ai/docs/documentation/span-01/quickstart): the target method is the output, its saved Perch context is the input, and all applicable checks are scored as behaviors in one call. Issue scores use `p_present`; semantic lint uses `p_absent` for the rule being satisfied. `p_not_observable` is saved separately. This is a different model interface from Jev's questions.
 
@@ -162,4 +160,3 @@ python3 span01.py --domain security --predictions security-span01.jsonl --report
 
 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.
 
-[Perch website](https://perchscan.com)

question-pack.json

@@ -1,7 +1,7 @@
 {
   "schema_version": 1,
   "source": "lakeday-org/perch/scan.yaml",
-  "source_sha256": "093bbc7ce61c7bd1049b42e3c1bb8ee783a729adec44f26c7024760c9192a187",
+  "source_sha256": "f881306f892af4c36b306480cc059ad86affdd196ca99f7b1a755906f4513cee",
   "domains": {
     "bug": [
       {
@@ -14,7 +14,7 @@
           "label": "kind",
           "pick": "strongest"
         },
-        "min": 60,
+        "min": 50,
         "when": null,
         "question": {
           "type": "noul",
@@ -26,66 +26,23 @@
         }
       },
       {
-        "name": "kind",
-        "type": "choice",
-        "language": null,
-        "where": "**/*",
-        "issue": null,
-        "min": null,
-        "when": null,
-        "question": {
-          "type": "choice",
-          "instructions": "If `method` has a defect, which kind is it?",
-          "criteria": {
-            "boundary": "An off-by-one, a wrong comparison, or a mishandled edge of a range",
-            "missing_null_handling": "A null, undefined, empty, or absent value is not handled",
-            "wrong_return": "The wrong value, or a value of the wrong shape, is returned for some input",
-            "swallowed_error": "An error is caught, ignored, or turned into a misleading result",
-            "state_mutation": "Shared or input state is mutated when it should not be, or left stale when it should change",
-            "ordering": "Operations run in the wrong order, or rely on an order that is not guaranteed",
-            "resource_leak": "A handle, lock, timer, process, or connection is not released on some path",
-            "inverted_condition": "A condition, sign, or boolean is the wrong way round"
-          }
-        }
-      },
-      {
-        "name": "severity",
-        "type": "score",
-        "language": null,
-        "where": "**/*",
-        "issue": null,
-        "min": null,
-        "when": null,
-        "question": {
-          "type": "score",
-          "instructions": "If something is wrong with `method`, how much would a caller feel it?",
-          "criteria": [
-            "No caller would notice",
-            "A wrong result in a rare case, or one the caller can see and recover from",
-            "A wrong result or wrong state in ordinary use",
-            "Data lost, corrupted, or exposed, or a check that should stop someone bypassed"
-          ]
-        }
-      },
-      {
-        "name": "does_what_it_claims",
+        "name": "bug_edge_case",
         "type": "noul",
         "language": null,
         "where": "**/*",
         "issue": {
           "type": "defect",
-          "label": "does_not_do_what_it_claims",
-          "on": false,
+          "label": "kind",
           "pick": "strongest"
         },
-        "min": 70,
+        "min": 50,
         "when": null,
         "question": {
           "type": "noul",
-          "instructions": "Does `method` do what its name, its parameters and its `leading_comment` say it does?",
+          "instructions": "Does `method` produce a wrong result for a valid empty, zero, first, last, or missing input?",
           "criteria": {
-            "true": "It behaves the way its name and its comment say it does",
-            "false": "It does something different from, less than, or more than its name and comment say, so one of the two is wrong"
+            "true": "One of these valid inputs produces an incorrect result or failure.",
+            "false": "These valid inputs produce the intended results and errors."
           }
         }
       }
@@ -791,7 +748,7 @@
       }
     ]
   },
-  "pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d",
-  "protocol_id": "perch-scan-bb53c8a6ca30",
-  "binary_protocol_id": "perch-scan-binary-bb53c8a6ca30"
+  "pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc",
+  "protocol_id": "perch-scan-afe116cc8461",
+  "binary_protocol_id": "perch-scan-binary-afe116cc8461"
 }

results-current.jsonl

@@ -1,9 +1,9 @@
-{"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.4620038040696911, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 1752, "fp": 87, "tn": 2492, "tp": 109}, "domain": "bug", "f1": 0.10597958191541079, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.6199362540000001, "inference_endpoint": "https://api.typesafe.ai/v1/systemone", "inference_host": "TypeSafe API", "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.4214365881032548, "pair_order_correct": 751, "pair_order_eligible": 1782, "precision": 0.5561224489795918, "protocol_id": "perch-scan-bb53c8a6ca30", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.05857066093498119, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "public"}
+{"alert_rule": "any issue score > max(run threshold, question min)", "average_precision": 0.47342616022901857, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 1532, "fp": 328, "tn": 2251, "tp": 329}, "domain": "bug", "f1": 0.26131850675139, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.5513116140000001, "inference_endpoint": "https://api.typesafe.ai/v1/systemone", "inference_host": "TypeSafe API", "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.4377104377104377, "pair_order_correct": 780, "pair_order_eligible": 1782, "precision": 0.5007610350076104, "protocol_id": "perch-scan-afe116cc8461", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.1767866738312735, "run_id": "perch-bug-two-questions-20260929", "threshold": 0.5, "track": "public"}
 {"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.44622354051764024, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 1061, "fp": 1023, "tn": 1556, "tp": 800}, "domain": "bug", "f1": 0.43431053203040176, "inference_cost_basis": "API reported", "inference_cost_usd": 0.239212, "inference_endpoint": "https://app.beam.cloud/v1/models/jev/semif/invoke", "inference_host": "Beam", "model_id": "semif", "model_revision": "beam:jev/semif@851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a source:ca3ba65f142967030ecb453346e94d6f476a69df", "pair_order": 0.4489337822671156, "pair_order_correct": 800, "pair_order_eligible": 1782, "precision": 0.43883708173340646, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.42987641053197206, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "public"}
 {"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.4681363756945909, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 1240, "fp": 698, "tn": 1881, "tp": 621}, "domain": "bug", "f1": 0.3905660377358491, "inference_cost_basis": "API reported", "inference_cost_usd": 0.239212, "inference_endpoint": "https://app.beam.cloud/v1/models/jev/diffusiongemma/invoke", "inference_host": "Beam", "model_id": "diffusiongemma", "model_revision": "beam:jev/diffusiongemma@ec4ff3df205028f4e81c954c2227f9312b3ec2ea source:3e296f085ce88e5da3850288646b3429eb476774", "pair_order": 0.5241301907968574, "pair_order_correct": 934, "pair_order_eligible": 1782, "precision": 0.47081122062168307, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.33369156367544334, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "public"}
 {"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.4206166543192416, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 336, "fp": 2087, "tn": 492, "tp": 1525}, "domain": "bug", "f1": 0.5572811986113649, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.21036392983052288, "inference_endpoint": null, "inference_host": "Runpod pod, H100 80 GB, US-NE-1", "model_id": "clm", "model_revision": "Contrastive-LM/CLM-v0.1-8B@head-b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "pair_order": 0.45286195286195285, "pair_order_correct": 807, "pair_order_eligible": 1782, "precision": 0.422203765227021, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.8194519075765717, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "public"}
 {"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.43783211394630184, "benchmark_methods": 4440, "benchmark_pairs": 2220, "confusion": {"fn": 1390, "fp": 583, "tn": 1996, "tp": 471}, "domain": "bug", "f1": 0.32315608919382505, "inference_cost_basis": "API-reported input tokens at published rate", "inference_cost_usd": 0.18229642000000001, "inference_endpoint": "https://api.respan.ai/api/v1/scores", "inference_host": "Respan API", "model_id": "span01", "model_revision": "span-01-pro (API revision not published)", "pair_order": 0.4500561167227834, "pair_order_correct": 802, "pair_order_eligible": 1782, "precision": 0.4468690702087287, "protocol_id": "respan-span01-pro-perch-pack-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.25308973670069856, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "public"}
-{"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.46965237444275143, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 396, "fp": 26, "tn": 546, "tp": 28}, "domain": "bug", "f1": 0.11715481171548117, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.138020904, "inference_endpoint": "https://api.typesafe.ai/v1/systemone", "inference_host": "TypeSafe API", "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.48129675810473815, "pair_order_correct": 193, "pair_order_eligible": 401, "precision": 0.5185185185185185, "protocol_id": "perch-scan-bb53c8a6ca30", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.0660377358490566, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "private"}
+{"alert_rule": "any issue score > max(run threshold, question min)", "average_precision": 0.47062948664262266, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 339, "fp": 89, "tn": 483, "tp": 85}, "domain": "bug", "f1": 0.2842809364548495, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.122626728, "inference_endpoint": "https://api.typesafe.ai/v1/systemone", "inference_host": "TypeSafe API", "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.428927680798005, "pair_order_correct": 172, "pair_order_eligible": 401, "precision": 0.4885057471264368, "protocol_id": "perch-scan-afe116cc8461", "question_pack_sha256": "afe116cc8461552aaca996f8f0b639cdf5e42e9d8291c59995482309b5c330dc", "recall": 0.20047169811320756, "run_id": "perch-bug-two-questions-20260929", "threshold": 0.5, "track": "private"}
 {"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.44537360325188174, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 236, "fp": 239, "tn": 333, "tp": 188}, "domain": "bug", "f1": 0.4418331374853114, "inference_cost_basis": "API reported", "inference_cost_usd": 0.053726, "inference_endpoint": "https://app.beam.cloud/v1/models/jev/semif/invoke", "inference_host": "Beam", "model_id": "semif", "model_revision": "beam:jev/semif@851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a source:ca3ba65f142967030ecb453346e94d6f476a69df", "pair_order": 0.4239401496259352, "pair_order_correct": 170, "pair_order_eligible": 401, "precision": 0.44028103044496486, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.44339622641509435, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "private"}
 {"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.4793149154487596, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 272, "fp": 156, "tn": 416, "tp": 152}, "domain": "bug", "f1": 0.41530054644808745, "inference_cost_basis": "API reported", "inference_cost_usd": 0.053726, "inference_endpoint": "https://app.beam.cloud/v1/models/jev/diffusiongemma/invoke", "inference_host": "Beam", "model_id": "diffusiongemma", "model_revision": "beam:jev/diffusiongemma@ec4ff3df205028f4e81c954c2227f9312b3ec2ea source:3e296f085ce88e5da3850288646b3429eb476774", "pair_order": 0.5311720698254364, "pair_order_correct": 213, "pair_order_eligible": 401, "precision": 0.4935064935064935, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.3584905660377358, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "private"}
 {"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.42672500156530696, "benchmark_methods": 996, "benchmark_pairs": 498, "confusion": {"fn": 87, "fp": 458, "tn": 114, "tp": 337}, "domain": "bug", "f1": 0.5529122231337161, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.047189746421441615, "inference_endpoint": null, "inference_host": "Runpod pod, H100 80 GB, US-NE-1", "model_id": "clm", "model_revision": "Contrastive-LM/CLM-v0.1-8B@head-b2b4a8c9c2d39263eff78a351eb909a342ce9b3bf21a3f07c1d1bf15f1c4eda5", "pair_order": 0.47880299251870323, "pair_order_correct": 192, "pair_order_eligible": 401, "precision": 0.4238993710691824, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.7948113207547169, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "private"}
@@ -28,8 +28,8 @@
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results.jsonl

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