Add Span-01 results and benchmark runner

README.md

@@ -6,10 +6,21 @@ viewer: false
 
 # Perch benchmark results
 
-This benchmark compares [TypeSafe's Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), a [System One classifier](https://typesafe.ai/), 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](https://github.com/lakeday-org/perch): 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.
+This benchmark compares [TypeSafe's Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), a [System One classifier](https://typesafe.ai/), with other classifiers 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](https://github.com/lakeday-org/perch): 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.
 
+## Inference source
+
+| Model | Where these results were scored |
+| --- | --- |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | [TypeSafe API](https://api.typesafe.ai/v1/systemone) |
+| [DiffusionGemma Jev](https://github.com/razorback16/openjev) | [Beam endpoint](https://app.beam.cloud/v1/models/jev/diffusiongemma/invoke) |
+| [SemiF](https://github.com/TheoLeeCJ/SemIf) | [Beam endpoint](https://app.beam.cloud/v1/models/jev/semif/invoke) |
+| [CLM-v0.1-8B](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B) | Runpod H100 80 GB pod (US-NE-1) |
+| [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | [Respan scoring API](https://api.respan.ai/api/v1/scores) (`span-01-pro`) |
+
+
 ## Reading the results
 
 - **AP** measures how well scores rank actual issues across cutoffs.
@@ -29,6 +40,7 @@ Pairs come from open-source projects in Rust, JavaScript, TypeScript, Python, Go
 | [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 |
+| [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 |
 
 _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN are better._
@@ -39,6 +51,7 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
 | [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 |
+| [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 |
 
@@ -54,8 +67,9 @@ _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 |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [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 |
+| [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 |
+| [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 47.6% | 48.6% | 21.0% | 29.3% | 240 | **254** | 902 | 43.7% | $0.251 |
 | [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 |
 
@@ -66,7 +80,8 @@ _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) | **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 |
+| [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 |
+| [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 47.8% | 45.1% | 8.0% | 13.6% | 32 | **39** | 366 | 50.5% | $0.091 |
 | [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 |
 
@@ -80,8 +95,9 @@ _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 |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **95.0%** | **99.1%** | 50.0% | 66.5% | 107 | **1** | 107 | **96.3%** | — |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **95.0%** | **99.1%** | 50.0% | 66.5% | 107 | **1** | 107 | **96.3%** | $0.048 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 87.0% | 91.1% | **52.8%** | **66.9%** | **113** | 11 | **101** | 86.4% | $0.018 |
+| [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 79.6% | 81.7% | 50.0% | 62.0% | 107 | 24 | 107 | 88.3% | $0.014 |
 | [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 |
 
@@ -91,7 +107,8 @@ _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 |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **94.3%** | **97.4%** | **53.5%** | **69.1%** | **38** | **1** | **33** | **94.4%** | — |
+| [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) | **94.3%** | **97.4%** | **53.5%** | **69.1%** | **38** | **1** | **33** | **94.4%** | $0.016 |
+| [Span-01](https://www.respan.ai/docs/documentation/span-01/concept) | 83.2% | 84.2% | 45.1% | 58.7% | 32 | 6 | 39 | 91.5% | $0.005 |
 | [DiffusionGemma Jev](https://github.com/razorback16/openjev) | 83.0% | 86.4% | **53.5%** | 66.1% | **38** | 6 | **33** | 85.9% | $0.006 |
 | [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 |
@@ -101,7 +118,9 @@ _Sorted by AP. Bold marks the best value in each quality column; lower FP and FN
 
 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.
 
-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.
+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.
+
+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); for semantic lint, input-token usage was measured by replaying the same 570 requests with `jev-1.13.0`. Span-01 costs use API-reported input tokens at [Respan's published rate](https://www.respan.ai/docs/documentation/span-01/concept). 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. 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.
 
@@ -111,6 +130,7 @@ JevLike (192-byte input), Laya (512-token input), and Kev (packed-branch limit)
 - [`results.jsonl`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/results.jsonl): all recorded result versions, identified by run and protocol ID.
 - [`predictions/current/`](https://huggingface.co/datasets/perchscan/benchmark-results/tree/main/predictions/current): public method probabilities from this run. Private predictions are not published.
 - [`benchmark.py`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/benchmark.py) and [`question-pack.json`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/question-pack.json): reproducible current-pack scorer and frozen questions.
+- [`span01.py`](https://huggingface.co/datasets/perchscan/benchmark-results/blob/main/span01.py): calls Span-01 with all applicable behaviors in one request per method, saves resumable predictions, and scores the completed dataset.
 - [`perch-lint-bench-public`](https://huggingface.co/datasets/perchscan/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:
@@ -120,6 +140,12 @@ python3 benchmark.py run --domain bug --scorer jev --predictions bug-jev.jsonl -
 python3 benchmark.py run --domain security --scorer jev --predictions security-jev.jsonl --report security-jev.json
 ```
 
+To run Span-01 on the public security pairs, set `RESPAN_API_KEY` and run:
+
+```sh
+python3 span01.py --domain security --predictions security-span01.jsonl --report security-span01.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.
 
 [Perch website](https://perchscan.com)

results-current.jsonl

@@ -1,24 +1,30 @@
-{"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, "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(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, "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, "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, "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": 0.46965237444275143, "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, "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(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, "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, "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"}
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results.jsonl

@@ -40,27 +40,33 @@
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+{"alert_rule": "any issue score > max(0.5, question min)", "average_precision": 0.4286620202416757, "benchmark_methods": 2622, "benchmark_pairs": 1311, "confusion": {"fn": 0, "fp": 1480, "tn": 0, "tp": 1142}, "domain": "security", "f1": 0.6068012752391073, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 1.4318487214883406, "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": 0.45907473309608543, "pair_order_correct": 516, "pair_order_eligible": 1124, "precision": 0.43554538520213576, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 1.0, "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": 0.4763745638492422, "benchmark_methods": 2622, "benchmark_pairs": 1311, "confusion": {"fn": 902, "fp": 254, "tn": 1226, "tp": 240}, "domain": "security", "f1": 0.293398533007335, "inference_cost_basis": "API-reported input tokens at published rate", "inference_cost_usd": 0.25124128, "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": 0.43683274021352314, "pair_order_correct": 491, "pair_order_eligible": 1124, "precision": 0.48582995951417, "protocol_id": "respan-span01-pro-perch-pack-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.21015761821366025, "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": 0.48923834174114805, "benchmark_methods": 878, "benchmark_pairs": 439, "confusion": {"fn": 316, "fp": 80, "tn": 400, "tp": 82}, "domain": "security", "f1": 0.29285714285714287, "inference_cost_basis": "API input tokens at published rate", "inference_cost_usd": 0.18228470400000002, "inference_endpoint": "https://api.typesafe.ai/v1/systemone", "inference_host": "TypeSafe API", "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.4020100502512563, "pair_order_correct": 160, "pair_order_eligible": 398, "precision": 0.5061728395061729, "protocol_id": "perch-scan-bb53c8a6ca30", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.20603015075376885, "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": 0.46783576019545, "benchmark_methods": 878, "benchmark_pairs": 439, "confusion": {"fn": 0, "fp": 480, "tn": 0, "tp": 398}, "domain": "security", "f1": 0.6238244514106583, "inference_cost_basis": "API reported", "inference_cost_usd": 0.074232, "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": 0.5, "pair_order_correct": 199, "pair_order_eligible": 398, "precision": 0.4533029612756264, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 1.0, "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": 0.49213375578819896, "benchmark_methods": 878, "benchmark_pairs": 439, "confusion": {"fn": 311, "fp": 89, "tn": 391, "tp": 87}, "domain": "security", "f1": 0.30313588850174217, "inference_cost_basis": "API reported", "inference_cost_usd": 0.074232, "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": 0.5150753768844221, "pair_order_correct": 205, "pair_order_eligible": 398, "precision": 0.4943181818181818, "protocol_id": "beam-system-one-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.2185929648241206, "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": 0.4685584144637907, "benchmark_methods": 878, "benchmark_pairs": 439, "confusion": {"fn": 0, "fp": 480, "tn": 0, "tp": 398}, "domain": "security", "f1": 0.6238244514106583, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.5279945121692223, "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": 0.5125628140703518, "pair_order_correct": 204, "pair_order_eligible": 398, "precision": 0.4533029612756264, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 1.0, "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": 0.47810684603893244, "benchmark_methods": 878, "benchmark_pairs": 439, "confusion": {"fn": 366, "fp": 39, "tn": 441, "tp": 32}, "domain": "security", "f1": 0.13646055437100213, "inference_cost_basis": "API-reported input tokens at published rate", "inference_cost_usd": 0.09076118000000001, "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": 0.5050251256281407, "pair_order_correct": 201, "pair_order_eligible": 398, "precision": 0.4507042253521127, "protocol_id": "respan-span01-pro-perch-pack-v1", "question_pack_sha256": "bb53c8a6ca30920140e2bf33b2905d159e7b1709b29d1ea896637905dbf9047d", "recall": 0.08040201005025126, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.5, "track": "private"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.9503065382620107, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 107, "fp": 1, "tn": 213, "tp": 107}, "domain": "lint", "f1": 0.6645962732919255, "inference_cost_basis": "replayed API input tokens at published rate", "inference_cost_usd": 0.048004614, "inference_endpoint": "https://api.typesafe.ai/v1/systemone", "inference_host": "TypeSafe API", "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.9626168224299065, "pair_order_correct": 206, "pair_order_eligible": 214, "precision": 0.9907407407407407, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.5, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "public"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.5001988302534378, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 145, "fp": 67, "tn": 147, "tp": 69}, "domain": "lint", "f1": 0.3942857142857143, "inference_cost_basis": "API reported", "inference_cost_usd": 0.018353, "inference_endpoint": "https://app.beam.cloud/v1/models/jev/semif/invoke", "inference_host": "Beam", "model_id": "semif", "model_revision": "semif", "pair_order": 0.5654205607476636, "pair_order_correct": 121, "pair_order_eligible": 214, "precision": 0.5073529411764706, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.32242990654205606, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "public"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.869561328005063, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 101, "fp": 11, "tn": 203, "tp": 113}, "domain": "lint", "f1": 0.6686390532544378, "inference_cost_basis": "API reported", "inference_cost_usd": 0.018353, "inference_endpoint": "https://app.beam.cloud/v1/models/jev/diffusiongemma/invoke", "inference_host": "Beam", "model_id": "diffusiongemma", "model_revision": "diffusiongemma", "pair_order": 0.8644859813084113, "pair_order_correct": 185, "pair_order_eligible": 214, "precision": 0.9112903225806451, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.5280373831775701, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "public"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.5053795675276925, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 131, "fp": 80, "tn": 134, "tp": 83}, "domain": "lint", "f1": 0.4403183023872679, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.0101391623837234, "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": 0.514018691588785, "pair_order_correct": 110, "pair_order_eligible": 214, "precision": 0.50920245398773, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": null, "recall": 0.3878504672897196, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "public"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.7958127706387785, "benchmark_methods": 428, "benchmark_pairs": 214, "confusion": {"fn": 107, "fp": 24, "tn": 190, "tp": 107}, "domain": "lint", "f1": 0.6202898550724638, "inference_cost_basis": "API-reported input tokens at published rate", "inference_cost_usd": 0.01389252, "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": 0.883177570093458, "pair_order_correct": 189, "pair_order_eligible": 214, "precision": 0.816793893129771, "protocol_id": "respan-span01-pro-perch-pack-v1", "question_pack_sha256": null, "recall": 0.5, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "public"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.9434782206832935, "benchmark_methods": 142, "benchmark_pairs": 71, "confusion": {"fn": 33, "fp": 1, "tn": 70, "tp": 38}, "domain": "lint", "f1": 0.6909090909090909, "inference_cost_basis": "replayed API input tokens at published rate", "inference_cost_usd": 0.015560958, "inference_endpoint": "https://api.typesafe.ai/v1/systemone", "inference_host": "TypeSafe API", "model_id": "jev", "model_revision": "jev-1.13.0", "pair_order": 0.9436619718309859, "pair_order_correct": 67, "pair_order_eligible": 71, "precision": 0.9743589743589743, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.5352112676056338, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "private"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.4887464253484338, "benchmark_methods": 142, "benchmark_pairs": 71, "confusion": {"fn": 41, "fp": 32, "tn": 39, "tp": 30}, "domain": "lint", "f1": 0.45112781954887216, "inference_cost_basis": "API reported", "inference_cost_usd": 0.005866, "inference_endpoint": "https://app.beam.cloud/v1/models/jev/semif/invoke", "inference_host": "Beam", "model_id": "semif", "model_revision": "semif", "pair_order": 0.5070422535211268, "pair_order_correct": 36, "pair_order_eligible": 71, "precision": 0.4838709677419355, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.4225352112676056, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "private"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.8296819071061596, "benchmark_methods": 142, "benchmark_pairs": 71, "confusion": {"fn": 33, "fp": 6, "tn": 65, "tp": 38}, "domain": "lint", "f1": 0.6608695652173913, "inference_cost_basis": "API reported", "inference_cost_usd": 0.005866, "inference_endpoint": "https://app.beam.cloud/v1/models/jev/diffusiongemma/invoke", "inference_host": "Beam", "model_id": "diffusiongemma", "model_revision": "diffusiongemma", "pair_order": 0.8591549295774648, "pair_order_correct": 61, "pair_order_eligible": 71, "precision": 0.8636363636363636, "protocol_id": "perch-semantic-ensure-v1", "question_pack_sha256": null, "recall": 0.5352112676056338, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "private"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.5141925432023778, "benchmark_methods": 142, "benchmark_pairs": 71, "confusion": {"fn": 35, "fp": 35, "tn": 36, "tp": 36}, "domain": "lint", "f1": 0.5070422535211268, "inference_cost_basis": "allocated GPU runtime by embedding rows", "inference_cost_usd": 0.0033639277067493522, "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": 0.5633802816901409, "pair_order_correct": 40, "pair_order_eligible": 71, "precision": 0.5070422535211268, "protocol_id": "clm-choice-head-v1", "question_pack_sha256": null, "recall": 0.5070422535211268, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "private"}
+{"alert_rule": "rule violation score > 0.7", "average_precision": 0.8320090658208488, "benchmark_methods": 142, "benchmark_pairs": 71, "confusion": {"fn": 39, "fp": 6, "tn": 65, "tp": 32}, "domain": "lint", "f1": 0.5871559633027523, "inference_cost_basis": "API-reported input tokens at published rate", "inference_cost_usd": 0.00456932, "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": 0.9154929577464789, "pair_order_correct": 65, "pair_order_eligible": 71, "precision": 0.8421052631578947, "protocol_id": "respan-span01-pro-perch-pack-v1", "question_pack_sha256": null, "recall": 0.4507042253521127, "run_id": "perch-scan-pack-configured-floors-20260928", "threshold": 0.7, "track": "private"}

span01.py

@@ -0,0 +1,253 @@
+#!/usr/bin/env python3
+"""Run Span-01 on one public Perch benchmark and score the saved predictions.
+
+Requires RESPAN_API_KEY for new calls. The key is only read from the process
+environment; it is never saved in predictions or reports. This script uses the
+same method/context layout and behavior definitions as the recorded run.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+import sys
+import time
+from urllib.error import HTTPError, URLError
+from urllib.request import Request, urlopen
+
+import benchmark as core
+
+
+API = 'https://api.respan.ai/api/v1/scores'
+LINT_URL = 'https://huggingface.co/datasets/perchscan/perch-lint-bench-public/resolve/main/benchmark.jsonl'
+MODEL = 'span-01-pro'
+REVISION = 'span-01-pro (API revision not published)'
+PROTOCOL = 'respan-span01-pro-perch-pack-v1'
+
+
+def load_data(domain: str, path: Path | None):
+    if domain != 'lint':
+        return core.load_pairs(domain, path)
+    raw = path.read_bytes() if path else urlopen(LINT_URL, timeout=120).read()
+    pairs = [json.loads(line) for line in raw.splitlines() if line]
+    if not pairs or len({pair['pair_id'] for pair in pairs}) != len(pairs):
+        raise ValueError('Empty or duplicate lint pairs')
+    return pairs, hashlib.sha256(raw).hexdigest()
+
+
+def questions_for(pack: dict, domain: str, language: str) -> list[dict]:
+    return core.detection_questions(core.scan_questions(pack, domain, language), domain)
+
+
+def issue_definition(q: dict) -> str:
+    criteria = q['question']['criteria']
+    issue_on = q['issue'].get('on', True)
+    present = ' '.join(criteria['true' if issue_on else 'false'].split())
+    absent = ' '.join(criteria['false' if issue_on else 'true'].split())
+    if issue_on:
+        question = ' '.join(q['question']['instructions'].split())
+        return (f'The target method has this issue: {question} '
+                f'Present when: {present} Absent when: {absent}')
+    return (f'The target method violates its stated behavior. '
+            f'Present when: {present} Absent when: {absent}')
+
+
+def method_parts(pair: dict, side: dict) -> tuple[str, str]:
+    state = side['state']
+    method = state.get('method') or {}
+    language = pair.get('language') or 'unknown'
+    context = [f'LANGUAGE: {language}']
+    for name, title in (('imports', 'IMPORTS'), ('module_scope', 'MODULE SCOPE'),
+                        ('call_graph', 'CALL GRAPH EDGES'), ('called_by', 'CALLERS'),
+                        ('calls', 'CALLEES')):
+        value = state.get(name)
+        if not value:
+            continue
+        if isinstance(value, list):
+            rendered = '\n'.join(
+                item if isinstance(item, str) else
+                f"{item.get('name', '')} ({item.get('path', '')})\n{item.get('source', '')}"
+                for item in value)
+        else:
+            rendered = str(value)
+        context.append(f'{title}:\n{rendered}')
+    target = (f"PATH: {method.get('path') or side.get('path') or ''}\n"
+              f"METHOD: {method.get('name') or side.get('method_name') or ''}\n"
+              f"{method.get('source') or side.get('source') or ''}")
+    return '\n\n'.join(context), target
+
+
+def behaviors_for(pair: dict, domain: str, pack: dict):
+    if domain == 'lint':
+        ensure = ' '.join(pair['rule']['ensure'].split())
+        return [{'id': 'rule_satisfied',
+                 'definition': 'The target method satisfies this requirement: ' + ensure}], []
+    questions = questions_for(pack, domain, pair['language'])
+    if not questions:
+        raise ValueError(f'No questions for {domain}:{pair["language"]}')
+    return [{'id': q['name'], 'definition': issue_definition(q)} for q in questions], questions
+
+
+def call_api(pair: dict, role: str, behaviors: list[dict], key: str):
+    context, target = method_parts(pair, pair[role])
+    payload = {'model': MODEL,
+               'span': {'input': [{'role': 'user', 'content': context}],
+                        'output': {'role': 'assistant', 'content': target}},
+               'behaviors': behaviors}
+    raw = json.dumps(payload, ensure_ascii=False).encode()
+    for attempt in range(9):
+        request = Request(API, data=raw, method='POST',
+                          headers={'Authorization': f'Bearer {key}',
+                                   'Content-Type': 'application/json'})
+        try:
+            with urlopen(request, timeout=120) as response:
+                result = json.load(response)
+            break
+        except HTTPError as exc:
+            if exc.code not in (429, 500, 502, 503, 504) or attempt == 8:
+                raise RuntimeError(f'Span-01 HTTP {exc.code} for {pair["pair_id"]}:{role}') from None
+            try:
+                seconds = float(json.loads(exc.read()).get('retry_after', 0))
+            except (ValueError, TypeError, AttributeError):
+                seconds = 0
+            time.sleep(max(seconds, min(60, 2 ** attempt)))
+        except URLError:
+            if attempt == 8:
+                raise RuntimeError(f'Span-01 network failure for {pair["pair_id"]}:{role}') from None
+            time.sleep(min(60, 2 ** attempt))
+    if result.get('model') != MODEL:
+        raise ValueError(f'Unexpected model: {result.get("model")}')
+    by_id = {item['id']: item for item in result['results']}
+    if by_id.keys() != {b['id'] for b in behaviors}:
+        raise ValueError('Missing or duplicate behavior result')
+    for item in by_id.values():
+        values = [item[k] for k in ('p_present', 'p_absent', 'p_not_observable')]
+        if not all(isinstance(p, (float, int)) and math.isfinite(p) and 0 <= p <= 1
+                   for p in values) or abs(sum(values) - 1) > .01:
+            raise ValueError('Invalid behavior probabilities')
+    return by_id, result.get('usage') or {}
+
+
+def predicted_row(pair: dict, role: str, domain: str, pack: dict, key: str):
+    behaviors, questions = behaviors_for(pair, domain, pack)
+    answers, usage = call_api(pair, role, behaviors, key)
+    if domain == 'lint':
+        scores = {'rule_violation': answers['rule_satisfied']['p_absent']}
+        unknown = {'rule_violation': answers['rule_satisfied']['p_not_observable']}
+        alert = scores['rule_violation'] > .7
+    else:
+        scores = {name: item['p_present'] for name, item in answers.items()}
+        unknown = {name: item['p_not_observable'] for name, item in answers.items()}
+        alert = any(scores[q['name']] > max(.5, (q.get('min') or 0) / 100)
+                    for q in questions)
+    return {'domain': domain, 'pair_id': pair['pair_id'], 'role': role,
+            'score': max(scores.values()), 'question_scores': scores,
+            'not_observable': unknown, 'alert': alert, 'usage': usage,
+            'model_revision': REVISION, 'protocol_id': PROTOCOL,
+            'pack_sha256': pack['pack_sha256'] if domain != 'lint' else None}
+
+
+def score_report(pairs: list[dict], predictions: dict, domain: str, pack: dict, digest: str):
+    if domain != 'lint':
+        report = core.report(pairs, predictions, domain, digest, REVISION,
+                             pack['protocol_id'], .5, pack)
+        report['protocol_id'] = PROTOCOL
+    else:
+        items = []
+        for pair in pairs:
+            for role in ('before', 'after'):
+                row = predictions[pair['pair_id'], role]
+                label = pair[role]['labels'][pair['domain']]
+                items.append((label, row['score'], row['score'] > .7))
+        tp = sum(y and a for y, _, a in items)
+        fp = sum(not y and a for y, _, a in items)
+        fn = sum(y and not a for y, _, a in items)
+        tn = sum(not y and not a for y, _, a in items)
+        eligible = correct = ties = 0
+        for pair in pairs:
+            if pair['before']['labels'][pair['domain']] and not pair['after']['labels'][pair['domain']]:
+                eligible += 1
+                a = predictions[pair['pair_id'], 'before']['score']
+                b = predictions[pair['pair_id'], 'after']['score']
+                correct += a > b
+                ties += a == b
+        precision = tp / (tp + fp) if tp + fp else 0.0
+        recall = tp / (tp + fn) if tp + fn else 0.0
+        report = {'domain': 'lint', 'model_revision': REVISION, 'protocol_id': PROTOCOL,
+                  'threshold': .7, 'alert_rule': 'rule violation score > 0.7',
+                  'benchmark_sha256': digest, 'benchmark_pairs': len(pairs),
+                  'benchmark_methods': len(items),
+                  'average_precision': core.average_precision([(y, s) for y, s, _ in items]),
+                  'precision': precision, 'recall': recall,
+                  'f1': 2 * tp / (2 * tp + fp + fn) if 2 * tp + fp + fn else 0.0,
+                  'confusion': {'tp': tp, 'fp': fp, 'tn': tn, 'fn': fn},
+                  'vulnerable_or_buggy_before_above_fixed_after':
+                  {'correct': correct, 'ties': ties, 'eligible': eligible}}
+    tokens = sum((row.get('usage') or {}).get('input_tokens', 0) for row in predictions.values())
+    report['input_tokens'] = tokens
+    report['inference_cost_usd'] = tokens * .02 / 1_000_000
+    report['methods_max_not_observable_over_50pct'] = sum(
+        max(row['not_observable'].values()) > .5 for row in predictions.values())
+    return report
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('--domain', choices=('bug', 'security', 'lint'), required=True)
+    parser.add_argument('--dataset', type=Path, help='Local benchmark.jsonl; defaults to public HF pairs')
+    parser.add_argument('--question-pack', type=Path, default=Path(__file__).with_name('question-pack.json'))
+    parser.add_argument('--predictions', type=Path, required=True)
+    parser.add_argument('--report', type=Path, required=True)
+    parser.add_argument('--limit', type=int, default=0, help='New method sides to score this run; 0 means all')
+    parser.add_argument('--score-saved', action='store_true', help='Only score complete saved predictions')
+    args = parser.parse_args()
+    if args.limit < 0:
+        parser.error('--limit must be nonnegative')
+    pack = core.load_pack(args.question_pack)
+    pairs, digest = load_data(args.domain, args.dataset)
+    expected = {(pair['pair_id'], role) for pair in pairs for role in ('before', 'after')}
+    predictions = {}
+    if args.predictions.exists():
+        for line in args.predictions.read_text().splitlines():
+            if not line:
+                continue
+            row = json.loads(line)
+            identity = row['pair_id'], row['role']
+            if (identity not in expected or identity in predictions or row['domain'] != args.domain
+                    or row.get('protocol_id') != PROTOCOL):
+                raise ValueError(f'Unknown, duplicate, or wrong-protocol prediction: {identity}')
+            predictions[identity] = row
+    pending = [(pair, role) for pair in pairs for role in ('before', 'after')
+               if (pair['pair_id'], role) not in predictions]
+    if args.score_saved and pending:
+        raise ValueError(f'Incomplete: {len(pending)} method sides missing')
+    if args.limit:
+        pending = pending[:args.limit]
+    if pending:
+        key = os.getenv('RESPAN_API_KEY')
+        if not key:
+            raise RuntimeError('Set RESPAN_API_KEY to run Span-01')
+        args.predictions.parent.mkdir(parents=True, exist_ok=True)
+        with args.predictions.open('a') as output:
+            for index, (pair, role) in enumerate(pending, 1):
+                row = predicted_row(pair, role, args.domain, pack, key)
+                predictions[pair['pair_id'], role] = row
+                output.write(json.dumps(row, sort_keys=True) + '\n')
+                output.flush()
+                if index % 100 == 0 or index == len(pending):
+                    print(f'Scored {len(predictions)}/{len(expected)} method sides', file=sys.stderr)
+    if predictions.keys() != expected:
+        print(f'Incomplete: {len(predictions)}/{len(expected)}; rerun to resume', file=sys.stderr)
+        return
+    result = score_report(pairs, predictions, args.domain, pack, digest)
+    args.report.parent.mkdir(parents=True, exist_ok=True)
+    args.report.write_text(json.dumps(result, indent=2, sort_keys=True) + '\n')
+    print(json.dumps(result, sort_keys=True))
+
+
+if __name__ == '__main__':
+    main()